{ "$schema": "https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json", "basics": { "name": "Jordan Kail", "label": "Staff Software Engineer at Together AI", "image": "https://www.jckail.com/images/headshot/headshot.webp", "email": "jckail13@gmail.com", "url": "https://www.jckail.com/", "summary": "With over 13 years of experience in AI, analytics and machine learning, I now lead the agents platform and data engineering at Together AI, building the agent harness, internal agents factory and proprietary IP behind them. Before that I built AI classifiers at Meta and model-governance frameworks at Prove, and turned data into impact for startups and Fortune 50 companies alike.", "location": { "city": "Denver", "region": "CO", "countryCode": "US" }, "profiles": [ { "network": "GitHub", "username": "jckail", "url": "https://github.com/jckail" }, { "network": "LinkedIn", "username": "jckail", "url": "https://www.linkedin.com/in/jckail/" } ] }, "work": [ { "name": "Together AI", "location": "San Francisco, CA", "position": "Staff Software Engineer", "url": "https://www.together.ai/", "highlights": [ "Tech lead for data engineering since joining: grew the team from just myself to 15+ engineers while setting technical direction for the data platform behind Together's AI acceleration cloud.", "Tech lead for the agents platform: created the agent harness, built an internal agents factory and used it to deliver production agents, and contributed to multiple patent-pending innovations in agent systems.", "Built and operate agents for infrastructure automation, finance automation and other internal workflows that augment the teams they serve, extending team capacity while improving on-call response and infrastructure maintenance.", "Build the data platform behind Together's AI acceleration cloud: the pipelines, storage, and telemetry that turn inference and training traffic into product, reliability, and capacity signals.", "Build tracing, replay and evaluation harnesses so internal teams can measure and regression-test LLM agents before they ship.", "Build tracing, replay, and regression-testing harnesses for LLM agents, giving internal teams a repeatable way to evaluate agentic features before they ship.", "Own core data platform services for inference, fine-tuning, and dedicated GPU cluster workloads, making platform usage measurable end to end.", "Develop streaming and batch pipelines that make model-serving and GPU-fleet telemetry queryable in near real time for capacity planning and reliability engineering.", "Design data infrastructure for large-scale training and inference workloads, covering dataset curation, lineage, and quality controls for open-model work.", "Partner with research, infrastructure, and product teams to standardize data contracts and lineage for training-data and evaluation workflows.", "Mentor engineers and set technical direction for the data platform and agents platform architecture." ], "startDate": "2025-02" }, { "name": "Prove Identity", "location": "Denver, CO", "position": "Staff Software Engineer - Data", "url": "https://www.prove.com/", "highlights": [ "Built agents and harnesses that govern the statistical models used for identity and fraud resolution; these became core frameworks.", "Built AI-driven Retrieval-Augmented Generation (RAG) chatbots with Airflow, LangChain, and OpenAI, automating 150+ human-hours weekly.", "Engineered AI-powered RAG chatbots leveraging Airflow, LangChain, and OpenAI APIs to automate customer service workflows, reducing manual efforts by 150 hours per week. Addressed NLP challenges in ambiguous query handling, driving a 20% improvement in first-contact resolution.", "Spearheaded a company-wide migration from legacy Java/Oracle infrastructure to a cloud-native Go/PostgreSQL stack, cutting API response times from 30s to 12ms and slashing operational costs by 95%. Overcame challenges with service downtime, seamlessly integrating AWS services (Athena, S3, EC2) for enhanced scalability.", "Managed and mentored 9 data engineers and 6 data scientists, driving a 20% improvement in project delivery timelines. Collaborated with the VP of Platform Engineering on critical initiatives, serving as the primary data liaison to ensure seamless cross-departmental coordination.", "Deployed an event-driven data streaming platform with Go, Kafka, and Flink, transitioning 1,200+ batch jobs to real-time processing. Reduced data availability lag from days to seconds, significantly boosting dashboard accuracy and enabling data-driven decision-making for executive teams.", "Implemented real-time telemetry services with Prometheus, Grafana, Splunk, and AWS CloudWatch, addressing complex monitoring needs. Reduced incident response times by 40% while maintaining 99.9% system uptime for mission-critical services.", "Designed and developed data pipelines using Spark, Airflow, and DBT to streamline ETL processes, improving performance by 35%. Reduced report generation timelines from days to hours, enhancing reporting capabilities for product managers and business teams.", "Established robust data governance frameworks with Apache Atlas, ensuring full GDPR and SOC2 compliance. Implemented metadata standards, lineage tracking, and access policies, reducing audit preparation times by 30%.", "Optimized CI/CD pipelines for Apache Beam and Kubernetes deployments, resolving bottlenecks and reducing deployment times by 30%. Improved release frequency to support faster feature rollouts and iterative development cycles.", "Migrated computationally intensive SQL queries from RDS to Spark DataFrames, improving query performance by 70% and reducing compute costs by 30%. Enabled seamless processing of large datasets for business-critical applications." ], "startDate": "2023-06", "endDate": "2025-01" }, { "name": "Meta | Facebook", "location": "Seattle, WA + Menlo Park, CA + Remote, USA", "position": "Senior Data Engineer", "url": "https://about.fb.com/", "highlights": [ "Built AI/ML classifiers and the data, feature and evaluation pipelines around them.", "Designed and deployed 100+ ML pipelines using Airflow, Spark, and PyTorch, integrating NLP and computer vision models. Improved ad targeting precision and personalized notifications, driving higher engagement across billions of users.", "Lead data engineer for Facebook Public Groups, Community Chats, and cross-platform initiatives, leading 10+ engineers. Collaborated with data science, ML, and hardware teams to align product goals, resulting in a 20% faster delivery of cross-platform features.", "Designed exabyte scale data models for Community Messenger to handle multi-platform data streams, increasing user engagement by 35% across Instagram, WhatsApp, Facebook, and Quest despite complex cross-platform dependencies.", "Developed modular frameworks for data pipelines, streamlining data flows for thousands of engineers. Reduced integration issues by 40% and accelerated feature deployments by 25% through automation and standardization.", "Built telemetry systems capable of processing 10M+ events/second, improving signal quality by 20%. Developed Jinja-based monitoring tools, reducing downtime by 15% and ensuring reliable system performance.", "Implemented graph and entity models to support 10 billion monthly interactions, ensuring seamless experiences across Instagram, WhatsApp, Facebook, and Quest. Addressed latency and consistency challenges to maintain real-time performance.", "Served as the liaison between Facebook Messenger, Groups, and the early precursor to the LLaMA project, fine-tuning models for automated group management. Increased group engagement by 15% in pilot testing, paving the way for future LLM-powered features.", "Created from scratch QR code group invites, increasing join rates by 50%. Enabled offline engagement in multilingual regions, facilitating family reconnections and shelter logistics coordination during humanitarian efforts. Featured on Tech Crunch.", "Designed KPI dashboards to monitor DAUs, MAUs, and engagement trends using tools such as Tableau and internal data visualization frameworks. Enabled real-time insights, increasing engagement by 10% and retention by 12%.", "Engineered an automated framework for generating thousands of asynchronous Spark data pipelines, increasing compute efficiency by 66%. Overcame orchestration challenges, improving resource utilization and processing times." ], "startDate": "2021-01", "endDate": "2022-09" }, { "name": "Deloitte", "location": "Atlanta, GA + Menlo Park, CA", "position": "Consultant - AI & Advanced Analytics", "url": "https://www2.deloitte.com/ch/en/pages/strategy-operations/solutions/analytics-and-cognitive.html", "highlights": [ "Automated approximately 31% of human processed healthcare claims using transformer machine learning models, saving 250,000+ hours annually.", "Optimized exabyte-scale video reliability metrics, reducing daily processing time by 90% while expanding metric coverage.", "Automated 31% of healthcare claims processing with transformer-based ML models, addressing data inconsistencies and regulatory constraints. Saved 250,000+ human-hours annually and improved claim accuracy by 20%", "Optimized exabyte-scale video reliability metrics using distributed data processing frameworks. Cut daily processing time by 90% and expanded metric coverage across multiple product lines, improving monitoring precision.", "Led teams of 20+ consultants on high-profile Fortune 50 engagements, delivering advanced technical solutions aligned with client needs. Achieved 100% on-time project delivery across multiple engagements.", "Developed a risk detection service using machine learning algorithms to flag high-value insurance accounts. Reduced billing errors by 30% and improved revenue recovery through early detection of anomalies.", "Engineered NLP models inspired by Google's Transformer architecture within six months of its release. Reduced billing errors by 20% by implementing cutting-edge language models for document processing.", "Implemented massively parallel data pipelines using Spark and asynchronous frameworks, reducing healthcare claim turnaround times by 40%. Improved processing efficiency for high-volume workloads.", "Re-architected live video infrastructure to align with emerging short-form content trends, saving $100 million and 14 months of development time. Ensured seamless adoption of new formats across the platform.", "Optimized caching strategies, leveraging Redis and CDN-layer optimizations to cut costs by $10 million annually. Enhanced content delivery efficiency and reduced latency for high-traffic web services.", "Built predictive analytics models for server uptime using time-series forecasting techniques, improving video delivery reliability by 30% and enhancing user experience through proactive maintenance.", "Revamped A/B testing frameworks for billions of daily users, resolving data inconsistencies and enabling more precise feature rollouts. Accelerated data-driven decision-making with improved statistical significance tracking.", "Leveraged Python, SQL, Java, TensorFlow, PyTorch, Spark, Airflow, Docker, and Kubernetes to develop and deploy scalable technical solutions. Delivered projects across machine learning, real-time analytics, and data pipeline automation for Fortune 50 clients." ], "startDate": "2018-12", "endDate": "2020-12" }, { "name": "Wide Open West", "location": "Denver, CO", "position": "Senior Data Engineer", "url": "https://www.wowway.com/", "highlights": [ "Built Machine Learning applications using custom classification and churn models, driving a 22% YoY increase in customer package upgrades.", "Led a team of 5 data practitioners, providing BI and data insights to sales, product, and engineering teams company-wide.", "Built machine learning models, including custom classification and churn prediction algorithms (e.g., logistic regression, K-means clustering). Increased customer package upgrades by 22% YoY and reduced churn by 15%.", "Managed a team of 5 data practitioners, providing BI and insights to sales, product, and engineering teams. Established KPIs and standardized reporting through governance committees, driving a 10% increase in sales performance.", "Developed a Kafka-powered real-time analytics platform, streaming data from field technicians and delivering instant job updates via a custom web portal. Reduced service completion times by 40%, replacing 20-minute phone calls with real-time notifications.", "Built scalable, cloud-based data solutions leveraging AWS services (SageMaker, S3, Redshift, and Athena). Improved data accessibility and reduced query times by 30% across sales and operations teams.", "Automated marketing campaigns using SendGrid to engage at-risk customers, reducing churn by 15%. Implemented multi-channel unsubscribe mechanisms, ensuring 100% compliance with communication preferences.", "Delivered geospatial insights using GIS tools to support sales in Arkansas and Alabama. Optimized resource allocation down to the city block level, driving an 18% increase in sales conversions and empowering door-to-door teams.", "Developed a dynamic revenue forecasting tool using Python and SQL to set bonus targets and calculate commissions by territory. Increased sales velocity and retention, driving a 12% increase in quarterly sales.", "Created a sales funnel dashboard with Tableau to monitor add-on targets, conversions, and installations. Identified millions in unrealized losses, leading to strategic reallocations and improved market performance.", "Applied machine learning models and geospatial analytics to optimize network NUC performance, reducing infrastructure build-out costs by 25%. Implemented continuous deployment with GitHub, ensuring code quality through design principles and best practices.", "Technologies Used: Python, Java, JavaScript, SQL, Flask, AWS (SageMaker, S3, EC2, Redshift, Athena), Apache Kafka, SendGrid, GIS tools." ], "startDate": "2017-11", "endDate": "2018-12" }, { "name": "Common Spirit Health", "location": "Denver, CO", "position": "Data Engineer", "url": "https://www.commonspirit.org/", "highlights": [ "Architected and delivered new rest APIs and data lakes, improving data processing time for external partner data products from 7 days to 5 minutes.", "Developed machine learning pipelines using Python and SQL to forecast hospital procedures, billing, and staffing. Reduced billing turnaround from 14 to 7 days.", "Architected and delivered new REST APIs and data lakes, reducing external partner data processing time from 7 days to 5 minutes. Enhanced data accessibility and scalability through efficient data structures.", "Managed tier-one vendor data extracts containing patient records, financial data, and ICD codes. Improved extract performance by 40% and ensured 100% HIPAA compliance through encryption and access control measures.", "Developed a data quality dashboard using Tableau and Python, enabling real-time failure detection. Reduced issue resolution time by 50% and improved overall data integrity and operational reliability.", "Conducted advanced data analysis using Dimensional Fact Models in SMP and MPP environments, improving query performance by 35%. Delivered actionable insights to executives, enhancing decision-making processes.", "Developed flexible big data extracts and real-time CDC lakes using AWS Redshift and Kafka. Enabled faster product delivery, reducing time to market by 20%.", "Designed complex data models for highly sensitive UII data, employing encryption and role-based access control. Ensured data security while supporting high-stakes analytics and compliance use cases.", "Created analytics dashboards using Qlik Sense, Tableau, and Python to track key metrics. Delivered actionable insights to executives, increasing reporting efficiency by 25% and enhancing operational visibility.", "Developed optimized storage and compute solutions, reducing third-party vendor data costs by 45%. Earned recognition from the VP of Business Intelligence for faster issue resolution and improved efficiency.", "Migrated data from relational to columnar formats (e.g., Parquet) using Redshift, improving query speed by 40% and enabling large-scale data processing for analytics.", "Developed machine learning pipelines using Python and SQL to forecast hospital procedures, billing, and staffing. Reduced billing turnaround from 14 to 5 days, optimized surgical room usage by 3000%, and enabled predictive staffing to improve patient care.", "Worked with orchestration tools similar to Airflow and utilized cloud technologies (Microsoft Azure and AWS) for data infrastructure, achieving seamless cloud operations and reducing deployment times.", "Leveraged strong Python and SQL expertise for ETL/ELT processes, developing scalable solutions with continuous improvement and adhering to best coding practices." ], "startDate": "2016-09", "endDate": "2017-11" }, { "name": "AcuStream | R1", "location": "Boulder, CO", "position": "Software Engineer - Data", "url": "https://www.r1rcm.com/", "highlights": [ "Built a custom invoicing system leveraging rule-based algorithms and machine learning, driving over $300M in annual revenue.", "Migrated clients from SFTP to real-time APIs with Flask and Apache Kafka, reducing data delivery times by 50%.", "Developed a custom invoicing system using Python and rule-based algorithms with ML components, generating $300M+ in annual revenue. Improved invoice accuracy by 35% and automated processes to reduce manual effort by 60%.", "Partnered with executives on high-impact initiatives, driving a 200% increase in revenue and boosting client retention by 25%. Optimized operational strategies, improving profit margins from 41% to 78%.", "Collaborated with CFOs and revenue cycle directors at leading healthcare systems to implement automated reconciliation processes. Cut reconciliation times by 50% and achieved 98% client satisfaction through scalable data solutions.", "Built a financial reconciliation platform with Django and Celery, automating line-item invoicing and scheduling. Improved billing speed by 40% and streamlined operations despite complex client requirements.", "Led infrastructure migration to AWS, moving PostgreSQL to RDS and compute workloads to EC2. Reduced query latency by 30% and cut infrastructure costs by 25%. Implemented IAM-based security, improving compliance audit performance by 20%.", "Refactored 100+ code modules into optimized Python, leveraging multithreading, hashing, and compression techniques. Boosted system efficiency by 45%, resolving bottlenecks in data processing workflows.", "Diagnosed and resolved issues in legacy Java applications, reducing downtime incidents by 15%. Enhanced UI responsiveness by 20% through performance optimizations in JavaScript.", "Created a Django-powered cron job system with Celery for task orchestration and real-time tracking. Increased task completion rates by 30% through automated monitoring and recovery mechanisms.", "Migrated clients from SFTP to real-time APIs with Flask and Apache Kafka, reducing data delivery times by 50%. Enhanced data accessibility, streamlining client operations and improving service quality.", "Developed fault-tolerant systems with Spring Boot, implementing state consistency mechanisms such as transaction rollbacks. Reduced system fault impact by 35%, ensuring high availability during critical operations.", "Supported pre-sales efforts and customer onboarding with on-site integrations, accelerating implementation timelines by 20% and enhancing customer satisfaction.", "Technologies Used: Python, Java, JavaScript, SQL, Django, Flask, Celery, AWS (RDS, EC2, IAM), Apache Kafka, Spring Boot, PostgreSQL." ], "startDate": "2013-04", "endDate": "2016-09" } ], "projects": [ { "name": "AI Billing System", "description": "A proof-of-concept application for tracking and billing AI chat thread interactions with real-time cost metrics and analytics.", "highlights": [ "This application is designed to solve the challenge of monitoring and billing for AI model usage in chat-based applications. As organizations increasingly deploy AI assistants, understanding the costs associated with these interactions becomes critical for business planning and cost management." ], "keywords": [ "Python", "FastAPI", "React", "Pydantic", "SQLAlchemy", "Supabase", "Docker" ], "url": "https://github.com/jckail/ai_chat_billing_app" }, { "name": "Super Teacher", "description": "AI assistant to help teachers manage their classroom and create personalized lesson plans for students. Made with Python, FastAPI, and Pydantic.", "highlights": [ "Developed Super Teacher, an AI-powered web application that revolutionizes lesson planning for educators by providing personalized lesson plans tailored to individual student needs." ], "keywords": [ "Python", "FastAPI", "React", "Pydantic", "SQLAlchemy", "Supabase", "Docker", "GCP" ], "url": "https://github.com/jckail/superteacher" }, { "name": "Join Group via QR", "description": "Featured on TechCrunch - Won internal hackathon and created the ability for Facebook group admins to invite users to their groups by generating a QR Code. Used by millions daily.", "highlights": [ "" ], "keywords": [], "url": "https://techcrunch.com/2022/03/09/facebook-rolls-out-new-tools-for-group-admins-to-manage-their-communities-and-reduce-misinformation/" }, { "name": "Jobbr", "description": "Agentic AI job matching a resume to available jobs at tech companies. Scrapes 100,000 jobs in under 15 minutes, using Python, Langchain, FastAPI and OpenAI.", "highlights": [ "Developed Jobbr, an AI-powered web application that transforms recruitment by matching job postings with candidates' skills and resumes for greater efficiency and accuracy. Leveraged GPT-4 and Anthropic’s Claude to parse job postings from raw HTML into structured JSON, aligning job descriptions with resumes using advanced NLP techniques. Integrated SQLModel and SQLAlchemy for efficient data management, with Alembic migrations to ensure smooth database updates and version control. Deployed the platform using Docker to maintain consistent environments and follow DevOps best practices. Implemented a FastAPI-based modular API with private endpoints, Supabase authentication, and AI-powered role parsing and URL scraping to promote code reusability and scalability. Used LangChain for document embedding and similarity analysis, enhancing job matching precision with advanced AI capabilities. Followed CI/CD workflows with comprehensive testing to ensure code reliability and seamless deployments. Prioritized security through token management, Supabase authentication, and password hashing with Python-JOSE and Passlib, alongside email validation to ensure strong user management. Employed Pandas, BeautifulSoup4, lxml, and NumPy for data processing and parsing job postings efficiently. Demonstrated proficiency across AI/ML, web development, data engineering, and cloud integration, solving real-world recruitment challenges with an AI-first approach. Tech Stack: Python 3.9+, FastAPI, SQLAlchemy, Pydantic, Supabase, Docker" ], "keywords": [ "Python", "FastAPI", "LangChain", "OpenAI", "Web Scraping" ], "url": "https://github.com/jckail/Jobbr" }, { "name": "Portfolio Website", "description": "A fully custom-built portfolio site showcasing my skills and work. Made with FastAPI and React, hosted on GCP via CloudRun.", "highlights": [ "This website is a custom-built portfolio site that I created to showcase my work. It is built using FastAPI and React, and is hosted on Google Cloud Platform via CloudRun. I chose React and TypeScript on the front end with a FastAPI backend so the whole stack stays typed end to end, and deployed it to Cloud Run behind Terraform-managed infrastructure. The site features a clean and modern design, with sections for my projects, skills, and experience. It also includes a contact form that allows visitors to get in touch with me. Overall, I am very happy with how the site turned out, and I think it does a great job of highlighting my work and skills." ], "keywords": [ "TypeScript", "React", "Vite", "Python", "FastAPI", "Pydantic", "Docker", "GCP Cloud Run", "Terraform", "Supabase", "Anthropic Claude" ], "url": "https://github.com/jckail/portfolio" }, { "name": "Pointup.io", "description": "An AI web app to manage 'All of your loyalty points in one place,' powered by a Selenium-WebDriver agent in AWS via elastic beanstalk, lambda, and s3. ", "highlights": [ "Created PointUp, a loyalty management tool designed to centralize and optimize points from credit cards, airlines, and hotel programs. Engineered a backend system using Python and Selenium for web scraping, overcoming anti-bot protections with NordVPN IP rotation and human behavior simulation. Deployed the system on AWS EC2 with secure data storage in S3, ensuring scalability and reliability. Integrated Auth0 for user authentication and encrypted sensitive data, ensuring secure handling of user credentials. Designed modular bot classes for easy integration of new loyalty programs, enhancing extensibility and user experience. Overcame complex anti-scraping challenges while maintaining ethical considerations around automation. Demonstrated expertise in Python development, cloud infrastructure, and secure web scraping through a real-world solution." ], "keywords": [ "Python", "TypeScript", "Selenium", "AWS Lambda", "AWS S3", "Elastic Beanstalk", "Docker" ], "url": "https://github.com/jckail/point_bot" }, { "name": "Algo Crypto", "description": "Trading algorithm via scraped crypto, NASDAQ, and CPME rare minerals data. Made with Flask, Pandas, AWS, and SageMaker.", "highlights": [ "Developed a Python-based data analysis tool to collect, process, and analyze real-time cryptocurrency data from APIs (CryptoCompare, CoinMarketCap, Alpha Vantage). Engineered a multithreaded data ingestion system to efficiently handle multiple data sources and ensure low-latency processing. Leveraged AWS services (S3, Glue, Athena) to create a scalable cloud-native architecture capable of handling large datasets. Applied stepwise regression and K-means clustering to detect market trends and correlations between traditional assets and cryptocurrencies. Demonstrated advanced data engineering skills by building modular components focused on data acquisition and ML-powered reporting. Highlighted problem-solving and financial technology expertise, navigating API integration challenges and delivering actionable insights into cryptocurrency trends." ], "keywords": [ "Python", "Flask", "Pandas", "AWS", "SageMaker" ], "url": "https://github.com/jckail/crypto_trader" }, { "name": "goPilot", "description": "Agentic AI developer assistant and cli tool to help debug, develop, and compile apps written in GO. Built using python and go leveraging OpenAI assistants api.", "highlights": [ "Built GoPilot, a next-generation CLI tool to enhance development workflows and accelerate mastery of Go programming. Integrated GPT-4 for code insights and debugging, transforming the CLI into an intelligent development companion. Designed a modular system combining Go, Python, and Bash to manage file operations, context updates, and automated testing workflows. Implemented goci-lint and Go’s native testing framework to streamline linting and testing operations. Added web scraping capabilities to fetch the latest Go documentation, ensuring developers stay updated on best practices. Developed a customizable CLI interface with task toggles and output path definitions to adapt to diverse workflows. Demonstrated proficiency in Go, Bash scripting, and AI-enhanced development, showcasing innovative problem-solving and productivity enhancement. " ], "keywords": [ "Python", "Go", "OpenAI API", "CLI" ], "url": "https://github.com/jckail/goPilot" }, { "name": "Data Playground", "description": "An interactive data-engineering lab for pipeline DAGs, data models, SQL analytics, graphs, and vector similarity.", "highlights": [ "Built a reproducible synthetic commerce pipeline with seeded event generation, deduplication, schema and lifecycle checks, and SQL analytics. Explore acquisition and retention scenarios, inspect quarantined records, and trace conversion, collected revenue, and cohort retention to the queries that compute them. The public React lab serves generated Python runs without a database; a bounded FastAPI service supports custom simulations when configured. A separate synthetic commerce dataset connects products, customers, and purchases in a relationship graph and explains cosine similarity over handcrafted product feature vectors. An engineering workbench exposes executable dependency DAGs, retry and failure traces, model grains and contracts, and architecture decisions grounded in the implementation. The original PostgreSQL and Streamlit experiment remains in the source repository." ], "keywords": [ "Python", "SQL", "SQLite", "FastAPI", "Pydantic", "React", "TypeScript", "Docker" ], "url": "https://github.com/jckail/data_playground" } ], "skills": [ { "name": "Data Engineering", "keywords": [ "Airbyte", "Airflow", "Kafka", "Pulsar", "Cloud Composer", "Cloud Data Fusion", "Dataprep", "Dataproc", "DBT", "Flink", "Kinesis Firehose", "PubSub" ] }, { "name": "Development Tools", "keywords": [ "Airtable", "Figma", "Linux", "Playwright", "Selenium", "Retool" ] }, { "name": "Artificial Intelligence", "keywords": [ "Amazon Bedrock", "Chroma", "Claude AI", "Google Gemini", "Hugging Face", "Keras", "LangChain", "Mistral AI", "OpenAI", "PyTorch", "TensorFlow", "Vertex AI", "Agent Platforms", "Agent Harnesses", "Agent Evaluation", "LLM Evaluation", "AI Classifiers", "Model Governance", "RAG" ] }, { "name": "Cloud Computing", "keywords": [ "Azure", "AWS", "Google Cloud" ] }, { "name": "Databases", "keywords": [ "Cassandra", "DuckDB", "DynamoDB", "Elasticsearch", "Firestore", "MongoDB", "MySQL", "Neo4j", "Pinecone", "PostgreSQL", "Qdrant", "Redis", "RocksDB", "Supabase", "TimescaleDB" ] }, { "name": "Programming Languages", "keywords": [ "C++", "Go", "Java", "JavaScript", "PHP", "Python", "Rust", "Scala", "SQL", "TypeScript" ] }, { "name": "Big Data", "keywords": [ "Hadoop", "Iceberg", "Spark", "BigQuery", "Databricks", "Presto", "Snowflake", "Trino" ] }, { "name": "Web Development", "keywords": [ "CSS3", "Django", "FastAPI", "Flask", "Gin", "GraphQL", "HTML5", "Material UI", "Node.js", "React", "Redux", "SQLAlchemy", "Svelte", "Tailwind CSS", "Vite", "WebAssembly" ] }, { "name": "DevOps", "keywords": [ "Datadog", "Docker", "Git", "GitHub Actions", "Grafana", "Jaeger", "Kubernetes", "NGINX", "OpenTelemetry", "Prometheus", "Splunk", "Terraform" ] }, { "name": "Data Science", "keywords": [ "Jupyter", "Looker", "NumPy", "Pandas", "Scikit-learn", "Streamlit", "Tableau" ] } ], "meta": { "canonical": "https://www.jckail.com/resume.json", "version": "v1.0.0", "lastModified": "2026-10-02T01:48:13Z" } }