Vetted Apache Hive 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.”

“Candidate has hands-on experience at Cognizant building production-grade automation and integration solutions across Python ML services, Java microservices, Kafka, and Selenium-based UI testing. They stand out for a strong reliability mindset—covering failure modes, observability, flaky test hardening, and translating ambiguous payment-system business processes into resilient end-to-end automated workflows.”

“Business development leader from PDD (product design & innovation consultancy) with high-functioning Chief of Staff/EA-style experience coordinating six business owners and ~20 multi-region designers. Led annual conference/event strategy and logistics (budgets, travel/visas, speaking/booths) and implemented productivity systems like CRM migration, standardized calendar/email protocols, and time-blocking to speed reporting and decision-making.”

“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”

“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.”

“Data engineer with ~4 years of experience building Python-based data ingestion/processing services and real-time streaming pipelines (Kafka/PubSub + Spark Structured Streaming). Has deployed containerized data applications on Kubernetes with GitLab CI/Jenkins pipelines and applied GitOps to cut deployment time ~40% while reducing config drift. Also supported a legacy on-prem data warehouse/backend migration to GCP using phased migration and parallel validation to meet strict reliability/SLA needs.”

“AI/ML engineer who shipped a production blood-test report understanding and personalized supplement recommendation product, using a LangGraph multi-agent pipeline on AWS serverless with OCR via Bedrock and RAG over vetted clinical research. Also built end-to-end recommender system pipelines at ASANTe using Airflow (ingestion, embeddings/features, training, registry, batch scoring/monitoring) with KPI reporting to Tableau, with a strong focus on safety, evaluation, and measurable reliability.”

“AI Engineer with production experience building an LLM-powered conversational scheduling assistant (rules-based + OpenAI GPT agents) and improving responsiveness by ~40% through architecture optimization. Strong in orchestration (Airflow), containerized deployments, and data quality (Great Expectations/PySpark), with prior work automating population health reporting pipelines (Azure Data Factory → Snowflake) and delivering insights via Tableau to non-technical stakeholders.”

Sri Mounika Jammalamadaka

Screened

Mid-level AI/ML Engineer specializing in GenAI, LLMs, and data platforms

Fairfax, VA6y exp

“Built and helped deploy a production RAG-based LLM assistant for HVAC anomaly diagnostics, partnering closely with field engineers and operations teams to make AI outputs trustworthy in real workflows. Stands out for practical post-launch optimization work—improving retrieval quality, reducing hallucinations, and stabilizing non-deterministic behavior—which contributed to roughly a 40% reduction in diagnosis time.”