.png){kind=logo}
Vetted Recommender Systems Professionals
Pre-screened and vetted.
Junior Data Scientist specializing in NLP, OCR, and recommendation systems
Senior Python Developer / AI/ML Engineer specializing in cloud ML microservices
Lead Software Engineer & Manager specializing in Python and JS/TS full-stack systems
Mid-level Data Scientist / ML Engineer specializing in risk, fraud, NLP and recommender systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and GenAI pipelines
Senior Machine Learning Engineer specializing in GenAI and LLM-powered systems
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Mid-level Data Scientist specializing in ML, NLP, and scalable data pipelines
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Mid-level Data Scientist specializing in recommendations, search relevance, and NLP
Mid-level Data Scientist specializing in ML, NLP, and forecasting across finance and retail
Mid-level Data Engineer specializing in cloud data platforms and BI analytics
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Tushar Jayendra Mhatre
Intern Data Scientist/ML Engineer specializing in generative AI and ML platforms
“AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.”
Tushar Gwal
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and MLOps
“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.”
Sudheer koki
Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems
“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.”
Rui Zhao
Junior Machine Learning Engineer specializing in semantic search and retrieval systems
“Built and shipped a production RAG system (“TROJAN KNOWLEDGE”) for answering questions over technical PDFs, using a 3-stage retrieval stack (BM25 + FAISS + cross-encoder) to lift F1 from 71% to 84%. Drove major performance gains with a 3-level cache (memory/Redis/disk) cutting latency from ~200ms to ~10ms, and added Prometheus/Grafana monitoring plus LangChain-based fallback logic to handle OpenAI rate limits under load.”
Nagasaikumar Jampani
Mid-level AI/ML Engineer specializing in Generative AI and RAG pipelines
“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.”