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Vetted AWS Glue Professionals
Pre-screened and vetted.
Senior DevOps Engineer specializing in cloud-native CI/CD and Kubernetes
Mid-level AI/Software Engineer specializing in LLM agents and RAG systems
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Mid-level Full-Stack Software Engineer specializing in cloud-native data platforms
Senior Software Engineer specializing in cloud-native backend, ETL, and AI/ML on AWS
Senior Machine Learning Engineer specializing in Generative AI and NLP
Junior Software Engineer specializing in LLMs, RAG, and Knowledge Graphs
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Mid-level Data Engineer specializing in AWS lakehouse and Spark pipelines
Senior Data Engineer specializing in Cloud Data Platforms and Generative AI
Senior Full-Stack Developer specializing in Python, AWS, and data/ETL systems
Principal Data Scientist specializing in AI/ML forecasting and MLOps
Mid-Level Full-Stack & Cloud Engineer specializing in scalable distributed systems
Abhinav Sharma
Director-level Solutions Architect specializing in financial systems and cloud infrastructure
“Prudential solution architect/technical product owner with a strong blend of enterprise architecture, partner-facing API pre-sales, and analytics-driven optimization in insurance platforms. Stands out for supporting high-impact third-party onboarding efforts, building cloud/data solutions across AWS and Azure, and translating complex integration, security, and reporting requirements into partner-ready solutions.”
NEHA KOLAN
Mid-Level Software Engineer specializing in microservices and cloud data pipelines
“Full-stack engineer with end-to-end ownership across React/TypeScript frontends, Spring Boot/Node microservices, and production ops on Docker/Kubernetes and AWS (ECS/CloudWatch). Built real-time healthcare eligibility and analytics systems at Cigna and an early-stage seller onboarding platform at Flipkart, driving measurable performance gains (35–40% latency/throughput improvements) through event-driven Kafka pipelines, Redis caching, and strong reliability/observability practices.”
“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”
Sasi Katamneni
Mid-level Data Scientist / AI-ML Engineer specializing in Generative AI and LLM applications
“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”
Mounika Gunturu
Senior Python Full-Stack Developer specializing in cloud-native microservices and data platforms
“Backend/data engineer from Oliver Wyman who built and ran production Python (FastAPI) services on AWS (ECS/Lambda/API Gateway) supporting risk modeling and regulatory reporting. Strong in reliability/observability, Glue-based ETL with data quality controls, and legacy SAS-to-Python modernization with rigorous parity validation; also demonstrated measurable SQL performance wins and cost-control improvements in serverless scaling. Based in Raleigh, NC and can travel onsite for important Bethesda-area meetings.”
Swathi Sankaran
Senior Python Full-Stack Developer specializing in cloud, data engineering, and ML/GenAI
“Backend/data engineer with hands-on production experience building FastAPI services on AWS and implementing strong reliability/observability (CloudWatch, ELK, correlation IDs, alarms). Has delivered serverless + container solutions with IaC (CloudFormation/Terraform) and Jenkins CI/CD, and built AWS Glue/PySpark pipelines into S3/Redshift with schema-evolution and data-quality safeguards; demonstrated large-scale SQL tuning (45 min to 3 min on a 500M-row workload).”