Vetted Apache Hadoop Professionals

“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”

“AI Software Engineer (student employee) at University of Washington IT who helped deploy "Purple," a governed, explainable LLM platform on Azure used by 100,000+ students/faculty/staff. Independently led scalable reliability efforts by building automated agent quality/load/red-team testing and CI/CD health validation (Playwright/Node.js, Azure DevOps), and previously built an explainable AI scheduling assistant for clinical operations at Proliance Surgeons.”

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”

Preetham Dandu

Screened ReferencesModerate rec.

Junior Software Engineer specializing in backend, distributed systems, and cloud platforms

Stony Brook, NY1y exp

“MS candidate with strong backend/data engineering focus who builds research and data systems with production-grade rigor (reproducibility, observability, restartability). Has hands-on experience securing and scaling FastAPI-based gateways in front of Java microservices, leading SQL Server→Snowflake migrations with dual-write/feature-flag rollouts, and hardening Kafka-based fleet-tracking systems against out-of-order and duplicate events.”

Shane Weinstock

Screened

Senior Data Scientist & Product Analytics Leader specializing in ML and experimentation

Austin, TX17y exp

“Aspiring founder with ~15 years of experience across varied backgrounds, motivated by frustration with slow, change-resistant large organizations and a desire to bring innovative ideas to market. Familiar with how venture capital/accelerators function (though not directly worked in them) and expresses strong willingness to take entrepreneurial risks.”

“Backend Python engineer focused on production-grade automation and reliability, with hands-on experience designing scalable API systems on PostgreSQL and making pragmatic architecture calls (modular monolith over premature microservices). Demonstrated measurable performance wins (50–60% latency reduction) and strong operational rigor via observability, incremental rollouts/feature flags, and security patterns like JWT + RBAC + database row-level security.”

“Built and deployed a production LLM-powered support assistant for customer support agents using a RAG architecture over internal docs and past tickets, with human-in-the-loop review. Demonstrates strong applied LLM engineering focused on real-world constraints (hallucinations, latency, cost) using routing to smaller models, reranking, caching, and rigorous evaluation/monitoring (offline eval sets, A/B tests, KPI tracking).”

“AI/LLM engineer who built a production RAG-based Text2SQL engine using Qdrant, including creating the underlying business/DB documentation, generating a test dataset, and designing detailed SQL-quality metrics for validation. Also partnered with non-technical stakeholders on a speech recognition project to prioritize medical terminology, improving accuracy through targeted corpora, lookup-table correction, and fine-tuning with a modified loss function.”

“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”

“NLP/ML engineer with hands-on healthcare and support-ticket text experience, building clinical-note structuring and semantic linking systems using spaCy, BERT clinical embeddings, and FAISS. Emphasizes production-grade delivery (Airflow/Databricks, PySpark, Docker, AWS/FastAPI/Lambda) and rigorous validation via clinician-labeled datasets, retrieval metrics, and user feedback.”

“ML/NLP practitioner with healthcare payer experience at HCSC, focused on connecting messy unstructured clinical notes to structured claims/provider data to improve fraud-analytics workflows. Has hands-on experience fine-tuning transformers in AWS SageMaker, building large-scale embedding search with FAISS, and implementing robust entity resolution using golden datasets, precision/recall calibration, and production monitoring for drift.”

“Software engineer with connected-car/automotive production experience who owned an end-to-end remote door lock/unlock feature and introduced unit testing (GTest) plus rig/simulator validation. Also built and productionized an AI-native AWS cloud cost assistant (Lex + GPT-based LLM + Lambda + RAG/vector DB) with guardrails and achieved 94% evaluation accuracy. Helped replace a third-party solution with an in-house build, saving the company ~€9M.”

“Built and shipped an AI-powered driver ranking/assignment system at AffirmoAI using LLM intent classification + RAG over pgvector/Postgres, served via FastAPI with a React UI that explains scores. Drove measurable improvements through optimization and iteration (latency down to <800ms, adoption 60%→90%+) and implemented rigorous eval loops with dispatcher ground truth plus cold-start handling for new drivers.”

SREEJA REDDY Konda

Screened

Mid-level AI/ML Engineer specializing in NLP, MLOps, and predictive analytics

Kentwood, MI6y exp

“AI/ML Engineer at Fifth Third Bank who has shipped production fraud detection and risk analysis systems combining ML models with LLM-powered insights/explanations, including real-time monitoring, drift detection, and automated retraining under regulatory explainability constraints. Also built a hybrid-retrieval internal knowledge-base QA system (+20% top-5 relevance) and delivered a customer support chatbot that reduced first response time by 30% through strong stakeholder collaboration.”

sai Pavan

Screened

Mid-level AI/ML Engineer specializing in MLOps, NLP, and real-time ML pipelines

5y exp

“Built a production, real-time insurance claims document-understanding and fraud-detection pipeline using TensorFlow + fine-tuned BERT, deployed on AWS (SageMaker/Lambda/API Gateway) with automated retraining via MLflow and Jenkins. Addressed noisy documents and latency using augmentation and model distillation (3x faster), cutting claims ops manual review by ~50% and reducing fraudulent payouts.”