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Vetted Amazon EMR Professionals
Pre-screened and vetted.
Mid-level Data Engineer specializing in real-time streaming and ML feature pipelines
Senior Software Engineer specializing in backend systems and payment platforms
Mid-level AI/ML Engineer specializing in generative AI and data engineering
Mid-level AI & Machine Learning Engineer specializing in computer vision and MLOps
Mid-level Machine Learning Engineer specializing in GenAI, LLM agents, and MLOps
Staff Machine Learning Engineer specializing in Generative AI, MLOps, and Computer Vision
Engineering Manager specializing in cloud-native platforms across AdTech and FinTech
Mid-level Data Engineer specializing in cloud lakehouse and streaming analytics
Senior Software Developer specializing in Python, AWS, and Big Data
Mid-level AI Data Engineer specializing in real-time streaming and LLM-powered fraud analytics
Mid-level Data Engineer specializing in real-time streaming and cloud data platforms
Senior Data Engineer specializing in cloud data platforms and big data pipelines
Leela Tikkisetty
Mid-level Software Engineer specializing in ML platforms and cloud-native backend systems
“Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).”
Sahithi K
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
“Data engineer with experience at Moderna and Block owning high-volume (≈10TB/day) production pipelines on AWS, using Kafka/S3/Glue/dbt/Snowflake with strong data quality and observability practices (schema validation, anomaly detection, CloudWatch monitoring). Also built external financial API ingestion with Airflow retries, throttling/token rotation, and schema versioning, and helped stand up an early-stage biomedical data platform with CI/CD and incident debugging.”
Vismay Patel
Senior AI & Machine Learning Engineer specializing in NLP, GenAI, and MLOps
“ML/GenAI practitioner with healthcare domain depth who built and deployed a production cervical-cancer EMR classification system using a hybrid rules + medical BERT approach, optimized for high recall under severe class imbalance and PHI constraints. Experienced running end-to-end production ML/LLM pipelines with Apache Airflow (validation, promotion/rollback, monitoring, retraining) and partnering closely with clinicians to calibrate thresholds and implement human-in-the-loop review.”
jahnavi Vasala
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
“Data engineer with Intuit experience owning end-to-end, high-volume financial data pipelines (API/S3 ingestion, Airflow orchestration, Spark/PySpark + SQL transforms, Snowflake marts). Strong focus on reliability and data quality—achieved 99.8% SLA and cut discrepancies by 35% using Great Expectations, reconciliation, schema versioning, and automated backfills; also built near real-time Kafka/API data services with CI/CD and observability.”
Harsh Sanas
Intern-level Software Engineer specializing in GenAI, RAG, and backend systems
“AI/LLM engineer focused on shipping production-grade agents that automate support, sales intake, and ERP-connected workflows. Stands out for combining strong orchestration and guardrails with measurable business outcomes, including 45% faster support handling, ~$1.2M annual savings, 18% higher customer satisfaction, and 99.5%+ reliability in production.”
Biplob Bidari
Senior Data Engineer specializing in FinTech analytics and ML data platforms
“ML/AI engineer with Goldman Sachs experience building production fraud detection and RAG-based trading insights systems end-to-end. Stands out for combining real-time ML infrastructure, GenAI retrieval systems, and compliance-aware design, with measurable impact including nearly 25% false-positive reduction and improved analyst productivity.”
Praveen Nutulapati
Mid-level Generative AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems
“Built and deployed a production multi-agent RAG system at JPMorgan Chase to automate regulated credit analysis and compliance clause discovery across large internal policy/document libraries. Implemented LangGraph-based supervisor orchestration with structured state management (Azure OpenAI) to support long-running, resumable workflows, plus hybrid retrieval + re-ranking and guardrails for reliability. Strong at evaluation/observability (trace logging, LLM-judge, HITL) and at communicating results to non-technical stakeholders via Power BI embeds and Streamlit prototypes.”
Kunal Singh Pundir
Mid-level Full-Stack Developer specializing in cloud microservices and GenAI systems
“Built and owned an end-to-end AI-driven decisioning platform at Uber, combining LLM orchestration with typed tool contracts and a Snowflake-based RAG pipeline to make decisions fully auditable. Delivered large-scale measurable impact (120k requests/day, 18k cases auto-resolved/month) while improving ops SLA from 3 days to 6 hours and cutting incident response time nearly in half. Previously led a high-risk strangler-fig modernization of a legacy insurance platform across 120+ microsites at Accenture, coordinating across multiple squads with feature-flagged parallel cutovers.”