Senior Principal AI Engineer – GenAI & Agentic AI

About the job

We are looking for talented professionals with strong expertise in Generative AI, Agentic AI systems, and Machine Learning to design and build intelligent, scalable, and production-grade AI solutions. This includes working on LLM-based applications, multi-agent systems, and end-to-end ML pipelines. Key Responsibilities: 1. Machine Learning & Statistical Modelling • Build and optimize ML models (regression, classification, clustering, time series, sequence models) • Perform feature engineering, EDA, and data quality analysis • Apply statistical modelling, experimental design, and performance evaluation • Develop scalable ML pipelines for structured and unstructured data 2. GenAI / LLM Application Development • Develop GenAI applications using LangChain, LangGraph • Build and optimize RAG pipelines (retrieval, reranking, chunking, vector DBs like FAISS, OpenSearch, PGVector) • Design advanced prompt engineering strategies (ReAct, Chain-of-Thought, self-reflection loops) • Integrate LLM solutions with enterprise applications, APIs, and data systems 3. Agentic Systems & MCP • Design and implement agentic workflows (tool-calling agents, planner–executor systems, multi-agent architectures) • Build and manage Model Context Protocol (MCP) servers for tool integration • Implement memory architectures (episodic, semantic, vector-based memory) • Develop and evaluate agent systems using LangSmith 4. Evaluation, Observability & Quality • Define evaluation frameworks for ML and GenAI systems (precision@k, recall@k, grounding quality, hallucination checks) • Use LangSmith for tracing, monitoring, regression testing, and system-level evaluation • Ensure reliability, scalability, and transparency of AI systems 5. Cloud ML-Ops & Engineering • Manage ML lifecycle: model deployment, monitoring, data drift, and concept drift • Work across AWS / Azure / Databricks environments • Collaborate with engineering teams for production deployment • Follow best practices in Git, GitHub, Docker, and documentation 6. Leadership & Collaboration (for senior roles) • Drive technical direction and architecture decisions • Collaborate with product teams to define AI features and solutions • Mentor teams and contribute to AI strategy Required Skills & Experience: • 5–15 years of experience in Machine Learning, GenAI, and ML-Ops • Strong programming expertise in Python, PySpark, SQL • Experience with Scikit-Learn, XGBoost, LightGBM, Random Forest • Hands-on expertise with LangChain, LangGraph, LangSmith • Experience with RAG architectures and vector databases • Exposure to multi-agent systems and orchestration frameworks • Experience with MLflow / SageMaker / Databricks • Strong understanding of LLM evaluation, safety, and performance optimization • Proven experience building production-grade AI/GenAI systems