Vetted ChromaDB Professionals

“Worked on onboarding a Middle East logistics client processing thousands of invoices/month, building a production-ready pipeline that routes known vendor PDFs to deterministic regex parsers via Tax ID matching and falls back to LlamaParse for unknown layouts. Added financial consistency validation plus human-in-the-loop review and logging/metrics to continuously reduce LLM usage and improve template coverage.”

“At Esri, the candidate is building a production LLM-powered WebGIS AI framework that embeds an AI assistant into web maps and routes natural-language requests into ArcGIS JavaScript SDK functions via a LangGraph-orchestrated, multi-agent system. They emphasize production reliability and scale (strict tool calling/JSON, live schema validation, query guardrails) and rigorous evaluation/observability using LangSmith, offline prompt datasets, and latency/tool-call accuracy tracking.”

“ML engineer with production experience across healthcare and fraud domains, including end-to-end ownership of a telecare patient deterioration system at Oracle Health and a GPT-4/RAG fraud reporting solution at Cognizant. Stands out for combining scalable data/ML infrastructure, clinical NLP, and GenAI delivery with measurable gains in model quality and workflow efficiency.”

Dhananjay Sharma

Screened ReferencesModerate rec.

Mid-level Full-Stack Engineer specializing in AI and FinTech platforms

Stony Brook, NY6y exp

“Full-stack engineer who built RegArt’s product from 0→1 for enterprise compliance users at clients like HSBC and EY, including the production React frontend, backend APIs, and an LLM-powered search experience. Particularly compelling for startups needing someone who can move across UI, API, and data layers, make pragmatic architecture tradeoffs, and ship fast without over-engineering.”

“Built and deployed a production LLM-powered multi-agent compliance copilot (life sciences/finance) using LangChain/LangGraph + RAG over vector databases, delivered via async FastAPI on Kubernetes. Emphasizes audit-ready, deterministic outputs with schema constraints and citations, plus rigorous evaluation/monitoring; reports 60%+ reduction in manual research time and successful production adoption.”

“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).”

Anvith Reddy Dodda

Screened

Mid-level AI Engineer specializing in GenAI, NLP, and MLOps

Remote, USA3y exp

“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”

silin liu

Screened

Mid-level AI/ML Engineer specializing in LLM agents, RAG, and enterprise ML systems

New York City, NY5y exp

“Built a production multi-agent recommendation/RAG system for internal data analysts to speed up weekly report creation by improving document discovery and automating report/SQL generation. Implemented LangGraph-based orchestration with deterministic agent routing, robust error handling (interrupt/resume), and metadata-driven semantic chunking for diverse PDF/document formats, plus monitoring for latency, throughput, and token/cost efficiency.”

“Built and productionized an autonomous research agent (AutoGPT) in a Docker/Kubernetes environment with Pinecone-based long-term memory and custom Python tools for analysis, visualization, and report drafting. Implemented layered guardrails (prompt templates, automated validation, self-critique loops, and monitoring) and achieved ~25% reduction in manual report generation time while scaling the workflow to support multiple concurrent users.”

“GenAI/data engineering practitioner with production experience across Equinix, Optum, and Citibank—built an Azure OpenAI (GPT-4) + LangChain document intelligence platform processing 1.5M+ docs/month and a HIPAA-compliant Airflow healthcare pipeline handling 5M+ claims/day. Also delivered a real-time fraud detection + explainability system using LightGBM and a fine-tuned T5 NLG component, improving fraud accuracy by 15%+ while partnering closely with compliance stakeholders.”

“Early-stage founder building an engineering alpha product and planning a structured path to pilot and general availability. Active mentor in TechStars and MassChallenge with a strong VC network, emphasizing PMF, MVP-in-market feedback, and early sales while maintaining a sustainable approach to entrepreneurship.”

Pooja Dokuri

Screened

Mid-level AI/ML Engineer specializing in GenAI, RAG pipelines, and cloud MLOps

Remote, USA4y exp

“Built and deployed a production LLM + vector search clinical decision support system at UnitedHealth Group, retrieving medical evidence and patient context in real time for prior authorization and risk scoring. Strong in end-to-end RAG architecture (Hugging Face embeddings, Pinecone/FAISS, SageMaker, Redis) plus orchestration (Airflow/Kubeflow) and rigorous evaluation/monitoring, with demonstrated ability to align solutions with clinical operations stakeholders.”

“Built and shipped a production-grade agentic RAG system at CVS Health for patient adherence and medication recommendations, processing 20k+ patient records/day. Strong focus on real-world reliability: hybrid retrieval tuned with re-ranking (<400ms latency), strict JSON/schema validation and tool guardrails, and monitoring/drift detection that reduced MTTD from 6 days to 18 hours while improving recommendation accuracy (+8%) and cutting escalations (~23%).”