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Vetted LangChain Professionals
Pre-screened and vetted.
Intern Biomedical Data Scientist specializing in healthcare AI and LLM-based clinical NLP
Mid-level Generative AI & Machine Learning Engineer specializing in LLMs and RAG
Mid-level Data Engineer specializing in cloud-native big data pipelines and analytics
Mid-level Business Analyst specializing in FinTech, logistics, and data analytics
Senior Machine Learning Engineer specializing in LLMs and Generative AI
Mid-level Software Engineer specializing in distributed systems and GenAI infrastructure
Senior Full-Stack Engineer specializing in telehealth and commerce platforms
Mid-level Data Engineer specializing in big data platforms and analytics infrastructure
Executive CTO specializing in AI systems, FinTech, and high-scale platforms
Senior Software Engineer specializing in AWS distributed systems and developer tools
Mid-level AI Solutions Architect & Product Leader specializing in enterprise GenAI systems
Principal Data Scientist / AI Engineer specializing in healthcare-native AI platforms
Senior Full-Stack Engineer specializing in AI, cloud, and enterprise platforms
Mid-level Product Manager specializing in AI/ML data products for FinTech
Intern Software Engineer specializing in full-stack, cloud, and AI systems
Ashish Ernest Jeldi
Senior Data Scientist specializing in LLMs, agentic AI, and MLOps
“Built and shipped a production agentic LLM tool that helps internal teams update technical product whitepapers using plain-language edit requests, with strong guardrails (citations, verification, refusal/clarify flows) to reduce hallucinations and maintain compliance. Experienced taking LLM workflows from rapid LangChain prototypes to more predictable, debuggable LangGraph agent graphs, and orchestrating end-to-end ingestion/embedding/indexing/eval/deploy pipelines with Kubeflow.”
Venu Dave
Junior Software Development Engineer specializing in backend data platforms and LLM applications
“Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.”