Vetted Vector Databases Professionals

“AI engineer with experience taking a GPT-4-powered GenAI career coach toward production on Azure AI Foundry, re-architecting the backend with hybrid (vector + keyword) search and RAG optimizations to cut latency by 50%. Also has client-facing TCS experience building healthcare ETL pipelines and delivering error-free monthly reports, plus current work analyzing agentic system reasoning traces and guardrail drift as an AI research fellow.”

“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.”

Sudheer koki

Screened ReferencesStrong rec.

Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems

Florida, USA5y exp

“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”

“Built a production AI-native internal onboarding feature that reduced manual product setup effort by combining barcode API data, product photos, structured LLM outputs, and a polished real-time camera UI. Demonstrates hands-on experience across the full stack of LLM systems: prompt/schema design, multimodal inputs, backend orchestration with SQS and vector retrieval, and production reliability through evals, telemetry, and drift monitoring.”

Rathi Anand

Screened ReferencesStrong rec.

Senior Full-Stack Software Engineer specializing in Insurance, FinTech, and AI/ML applications

Dublin, CA17y exp

“AI/backend engineer who fine-tuned and deployed a production LLM chatbot using a LangChain + FAISS RAG pipeline, improving latency with PEFT/LoRA and driving strong business impact (40% customer adoption; 92% satisfaction). Also served as technical lead on a data aggregation system for underwriting/quoting, introducing GraphQL for more efficient, maintainable querying and applying CDC to keep cached ranking data fresh at scale.”

“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.”

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.”

“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”