Key Responsibilities :

- Design scalable AI/ML architectures covering Machine Learning, Deep Learning, Generative AI, LLMs, RAG, Agentic AI, NLP, and Computer Vision.

- Lead development of LLM-powered applications, advanced RAG pipelines, AI agents, and multi-agent automation workflows.

- Build and optimize ML models using Python, PyTorch, TensorFlow, Scikit-learn, and Hugging Face.

- Work with LLMs such as OpenAI, Claude, Gemini, Llama, Qwen, and Mistral, along with vector databases such as PostgreSQL/pgvector, Pinecone, and Milvus.

- Develop AI solutions using LangGraph, LangChain, AutoGen, CrewAI, or similar frameworks.

- Deploy and monitor AI/ML applications using AWS, Azure, or GCP, Docker, Kubernetes, CI/CD, and MLOps practices.

- Lead technical teams, conduct design and code reviews, mentor engineers, and ensure quality delivery.

- Participate in client consultations, technical pre-sales, solution design, POCs, estimations, and technical presentations.

- Research emerging AI technologies and optimize model performance, scalability, security, and infrastructure costs.

Required Skills :

- Strong Python programming and AI/ML model development expertise.

- Hands-on experience with Generative AI, LLMs, RAG, prompt engineering, embeddings, vector databases, and AI agents.

- Knowledge of NLP and/or Computer Vision, model evaluation, fine-tuning, and deployment.

- Experience with FastAPI, Flask, or Django; REST APIs; PostgreSQL or MongoDB.

- Hands-on experience with cloud platforms, Docker, CI/CD, and MLOps; Kubernetes and GPU-based deployments are advantageous.

- Strong technical leadership, problem-solving, communication, and client-handling skills.

Education & Preferred Qualifications :

- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, Engineering, or a related field.

- Proven experience delivering AI solutions from POC to production and leading engineering teams.

- Experience with enterprise GenAI, multi-agent systems, open-source LLMs, multimodal AI, and international clients is preferred.