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Vetted LangChain Professionals
Pre-screened and vetted.
Senior Full-Stack Python Engineer specializing in cloud microservices and AI/LLM systems
Senior Full-Stack Developer specializing in cloud-native microservices and AI-driven healthcare apps
Senior Full-Stack Engineer specializing in AI/ML product engineering
Senior Full-Stack Engineer specializing in React/TypeScript, React Native, and LLM-enabled products
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and scalable inference
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level Python Backend Developer specializing in cloud-native microservices and AI/ML platforms
Senior Full-Stack Software Engineer specializing in FinTech payments and risk systems
Mid-level Software Engineer specializing in backend APIs, data pipelines, and cloud microservices
Mid-level Applied AI Engineer specializing in LLMs, MLOps, and real-time AI systems
Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps
Principal business value leader specializing in AI, data, and cloud transformation
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Principal/Staff Engineer specializing in platform architecture, AI/ML, and distributed systems
Senior Software Engineer specializing in AI for Healthcare and Enterprise SaaS
Abhijay Sai Paladugu
Junior Data Scientist specializing in LLM agents, RAG, and reinforcement learning
“McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.”
Sai Chandra Bandi
Mid-level AI/ML Engineer specializing in Generative AI, LLM alignment, and RAG
“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”