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Niche LLM/agentic AI skills and seniority reduce applicant density despite Chennai metro location.
Deep ML/LLM and agentic AI expertise required, limiting cross-industry transferability.
Multiple mandatory ML/LLM, MLOps, and production deployment requirements make shortlisting highly selective.
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Design, build, and deploy scalable ML and AI solutions including forecasting, classification, NLP, anomaly detection, and optimization.
Develop and operationalize LLM-powered enterprise applications, autonomous AI agents, multi-agent systems, and RAG pipelines using vector databases.
Deploy and monitor AI workloads with Kubernetes, Docker, CI/CD, and MLOps pipelines, ensuring performance indicators like latency and hallucination risk are managed.
Strong hands-on expertise in ML frameworks (TensorFlow, PyTorch, Scikit-learn) and LLM frameworks (LangChain, LlamaIndex, CrewAI, AutoGen).
Experience with vector databases (Pinecone, FAISS, Chroma, Weaviate) and cloud platforms (AWS, Azure, GCP).
Proficient in Python programming and API development (FastAPI, Flask, REST, gRPC).
Work Experience Required: Not explicitly mentioned in the JD.
Experience building enterprise-grade AI solutions that integrate machine learning models with production-grade deployment and monitoring.
Skilled in autonomous AI agents and advanced generative AI technologies applied in business contexts.
Familiar with MLOps, container orchestration (Kubernetes, Docker), and building scalable inference services supporting AI workflows.