- AI Tool Development : Design and build agentic AI platform components, including tools, customized agents, and workflows that automate business processes.

- Generative AI & LLMs : Develop applications leveraging LLMs (OpenAI, Claude, LLaMA, Mistral) for code generation, text analysis, and content creation.

- RAG & Knowledge Systems : Build and enhance Retrieval Augmented Generation (RAG) pipelines using vector databases (FAISS, PGVector, Pinecone).

- AI-Augmented Development : Implement AI-assisted development workflows (e.g., using Cursor, LangChain, LangGraph) to accelerate coding, debugging, and testing.

- Production Deployment (MLOps) : Deploy AI models into production environments using Docker, Kubernetes, and cloud AI services (AWS SageMaker, Azure ML, Vertex AI).

- Performance Optimization : Implement evaluation pipelines to monitor agent quality, latency, cost, and reliability.

Required Technical Skills :

- Programming : Strong hands-on experience in Python (fastAPI/Flask).

- AI Frameworks : Proficiency in LangChain, LangGraph, LlamaIndex, or Semantic Kernel.

- Software Engineering : Knowledge of REST APIs, Git, CI/CD pipelines, and NoSQL/RDBMS databases (Preference, not necessary)

- Machine Learning : Solid understanding of Transformer architectures, embeddings, and NLP techniques.

- Cloud Platforms : In-depth experience with at least one major provider : AWS, Azure, or GCP.

Preferred Skills :

- Experience with LLM fine-tuning or customization methods.

- Familiarity with Model Context Protocol (MCP) or tool-integration frameworks.

Qualifications :

- Bachelors or Masters degree in Computer Science, AI/ML, or a related technical field.

- Proven track record of building and shipping production-grade AI systems (added advantage)