Key Responsibilities :

- End-to-end development of AI/ML solutions, with a focus on NLP, LLMs, and Generative AI applications.

- Design, develop, and deploy AI Agents and multi-agent systems to automate business workflows and decision-making processes.

- Architect and implement Model Context Protocol (MCP) servers and AI integration layers to enable secure and scalable connectivity between LLMs, enterprise applications, databases, APIs, and external tools.

- Drive innovation by leveraging frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, PyTorch, and TensorFlow.

- Architect and optimize machine learning pipelines, ensuring scalability, performance, and reliability.

- Collaborate with cross-functional teams to translate business objectives into AI-driven solutions.

- Oversee data engineering and model deployment on cloud platforms (Azure, GCP, AWS).

- Ensure robust API integration and seamless interaction between AI models, MCP servers, enterprise systems, and business applications.

- Monitor and continuously improve model performance, implementing best practices for MLOps.

- Mentor junior engineers and foster a culture of technical excellence and innovation.

Key Requirements :

- Experience: 6+ years of hands-on experience in machine learning and AI solution development, with a strong focus on NLP, LLMs, Generative AI, AI Agents, and Agentic AI architectures.

- Generative AI: Practical experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Llama, or similar frameworks.

- Hands-on experience designing and building AI Agents, multi-agent systems, RAG (Retrieval-Augmented Generation) applications, and enterprise AI assistants.

- Experience developing and deploying Model Context Protocol (MCP) servers, tool integrations, function calling frameworks, and agent orchestration platforms.

- Strong understanding of prompt engineering, vector databases, embeddings, semantic search, and AI workflow orchestration.

- Experience integrating LLMs with enterprise applications, APIs, databases, workflow engines, and business systems through secure and scalable architectures.

- Strong understanding of cloud platforms (Azure, GCP, AWS) and API development.

Preferred Qualifications :

- Experience building enterprise-grade Agentic AI solutions, AI copilots, autonomous workflow agents, and knowledge assistants.

- Experience with MCP-based architectures and integration of AI systems with enterprise tools such as CRM, ERP, HRMS, ITSM, document management, and workflow platforms.

- Knowledge of RAG frameworks, vector databases, and knowledge graph technologies.

- Experience implementing AI governance, guardrails, observability, security controls, and responsible AI practices for agent-based systems.

- Exposure to Azure OpenAI, AWS Bedrock, Google Vertex AI, Azure AI Foundry, or comparable enterprise AI platforms.