Vetted AWS Glue Professionals

“Data engineer in financial services (Northern Trust) who has worked across ingestion, transformation, data quality, orchestration, and serving on AWS (S3/Glue/EMR) with Airflow. Highlights include processing ~15M transactions with validation/anomaly detection for regulatory reporting and improving Snowflake query performance by 27% for risk/compliance reporting. Also built a personal real-time streaming service (FastAPI, Kafka, Redis, Cassandra) and uses production reliability patterns like blue-green/atomic swaps and robust retry strategies.”

Lohith Burra

Screened

Intern Technical Artist specializing in Unity real-time 3D visualization

Remote2y exp

“Unity/VR developer working on real-time Formula 1 broadcast simulations, including a 3D VR experience that lets users follow any car and compare qualifying laps using live F1 data. Built a proprietary math engine and JSON-based pipeline to drive accurate real-time car positioning in Unity; the 7-month effort became the team’s MVP and significantly increased app engagement.”

sai anuragh Sangoju

Screened

Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP

Dallas, Texas4y exp

“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”

Mark Wlodawski

Screened

Senior Unity Developer specializing in AI/LLM systems and multiplayer VR

Orlando, FL11y exp

“Backend/data engineer focused on AWS-native Python systems: built a FastAPI microservice on ECS/Fargate serving real-time analytics at millions of daily requests with strong reliability (OAuth2/JWT, retries/timeouts, correlation IDs) and autoscaling. Also delivered Glue/PySpark ETL pipelines to curated S3 Parquet/Athena with schema evolution + data quality controls, owned Airflow pipeline incidents, and has a track record of measurable performance and cost optimizations (e.g., ~80%+ query latency reduction; reduced logging/NAT/Fargate spend).”

“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”

“Analytics-focused candidate with hands-on experience in both supply chain data and AI product analytics. They have built SQL and Python pipelines for messy ERP/inventory data as well as high-volume user event data, and have driven experimentation, retention measurement, and dashboarding for AI avatar and voice/image cloning features.”

“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”

“Backend/data engineer with strong AWS production experience spanning serverless APIs and containerized workers (Lambda, API Gateway, ECS) plus data pipelines (Glue, S3, Athena/Redshift). Has modernized legacy SAS/cron batch systems into Python/AWS with parallel-run parity validation and low-risk cutovers, and has owned ETL incidents end-to-end (CloudWatch detection, backfills, and preventative controls). Targeting $130k–$150k base and strongly prefers remote, with occasional Bethesda onsite acceptable.”

“AWS-focused Python backend/data engineer who builds production analytics APIs and ETL pipelines using API Gateway, Lambda, Step Functions, ECS, Glue, S3, and RDS. Strong in operational reliability and performance tuning (including SQL indexing/partitioning) and has modernized legacy SAS statistical processing into validated Python services with phased rollouts and stakeholder sign-off.”

“Repeat founder/CTO with hands-on experience raising capital from friends and family, angels, corporate sources, federal grants, private equity, and venture capital. Built a startup in a software business incubator, later sold the company, and went on to serve as an Engineering Manager at the acquirer inside the Plug and Play accelerator ecosystem.”

“ML/NLP engineer focused on large-scale data linking for e-commerce-style catalogs and customer records, combining transformer embeddings (BERT/Sentence-BERT), NER, and FAISS-based vector search. Has delivered measurable lifts (e.g., +30% matching accuracy, Precision@10 62%→84%) and built production-grade, scalable pipelines in Airflow/PySpark with strong data quality and schema-drift handling.”

“AI/LLM practitioner who has deployed production chatbots across e-commerce, HRMS, and real estate, focusing on retrieval-first workflows for factual tasks like product and property search. Optimized intent understanding and significantly improved latency by using lightweight embeddings and tuning the inference pipeline on Groq (Llama 3.3), while applying modular orchestration and measurable production evaluation.”