Design and build end-to-end Gen AI and ML pipelines - from data exploration to production deployment
Develop agentic AI systems: RAG, tool orchestration, planning/execution flows
Build retrieval services, embedding pipelines, and model-routing infrastructure
Train, fine-tune, and evaluate LLMs and ML models; implement monitoring and cost-control frameworks
Translate data science research into robust, production-grade services
Collaborate with Engineering and Product; contribute to AI architecture decisions

5+ years in data science, ML engineering, or AI development
Strong Python with solid software engineering practices
Hands-on experience with Gen AI: LLMs, prompt engineering, RAG, embeddings, agents
Deep analytical skills — ability to explore, understand, and interpret complex datasets to select the right approach (statistical, ML, or AI-based)
Solid grounding in classical data science methods (statistical modeling, feature engineering, hypothesis testing) — knowing when not to use LLMs
Production deployment of ML/AI systems (APIs, microservices, cloud — AWS/Azure/GCP)
Familiarity with evaluation frameworks and model observability

Vector databases (Pinecone, Weaviate, FAISS)
LLM fine-tuning or RLHF experience
Databricks / large-scale data platforms
Agentic frameworks (LangChain, LlamaIndex, AutoGen)
Async/streaming architectures
Who You Are Curious and rigorous — you think in experiments but build for production. You bridge the gap between data science and engineering, and care about impact, quality, and reusable foundations.

PythonAWSAzureGCPCloudDatabricksLangChainLlamaIndexLLMGenAIAIMicroservices