Agentic AI Lead/Architect

We are looking for an experienced Agentic AI Lead / Architect with strong expertise in Python, AI/ML, Generative AI, and Agentic AI. The ideal candidate should have hands-on experience designing and developing intelligent AI agents, optimizing Generative AI applications, and implementing scalable AI solutions using modern AI frameworks.

The candidate will be responsible for defining the technical architecture, leading AI engineering initiatives, and building production-ready Agentic AI applications.

Key Responsibilities :

- Lead the design and architecture of Agentic AI and Generative AI solutions for enterprise use cases.

- Design and develop intelligent AI agents capable of reasoning, planning, decision-making, and executing multi-step tasks.

- Develop AI applications using Python and modern AI/ML technologies.

- Design and implement multi-agent workflows using frameworks such as LangChain, LangGraph, CrewAI, and AutoGen.

- Develop and optimize prompt engineering strategies to improve the accuracy, relevance, and reliability of GenAI applications.

- Design agent orchestration, tool calling, memory, workflow, and decision-making mechanisms.

- Evaluate and optimize LLM performance, latency, cost, and response quality.

- Integrate LLMs with enterprise applications, APIs, databases, tools, and external systems.

- Build RAG-based and knowledge-grounded AI applications where required.

- Establish best practices for AI application architecture, development, testing, deployment, and monitoring.

- Lead technical discussions, architecture reviews, and design decisions related to AI/ML and GenAI.

- Mentor AI/ML engineers and guide the team on Agentic AI development practices.

- Collaborate with product managers, architects, data scientists, software engineers, and business stakeholders.

- Identify emerging AI technologies and evaluate their applicability to business requirements.

- Ensure AI solutions are scalable, secure, maintainable, and production-ready.

Required Skills & Experience :

- Strong hands-on experience with Python.

- Strong understanding of Artificial Intelligence and Machine Learning concepts.

- Extensive experience with Generative AI and Large Language Models (LLMs).

- Strong expertise in Prompt Engineering and prompt optimization.

- Hands-on experience designing and optimizing GenAI and Agentic AI applications.

Experience with AI agent frameworks such as :

- LangChain

- LangGraph

- CrewAI

- AutoGen

- Strong understanding of agent orchestration, tool calling, memory, workflows, and multi-agent systems.

- Experience integrating LLMs with APIs, databases, enterprise applications, and external tools.

- Good understanding of RAG, embeddings, vector databases, and semantic search.

- Strong knowledge of AI application architecture and production deployment.

- Excellent problem-solving, analytical, and technical leadership skills.

Good to Have :

- Experience with LLMs such as GPT, Gemini, Claude, Llama, or other foundation models.

- Experience with vector databases such as Pinecone, FAISS, Weaviate, or Milvus.

- Knowledge of LLMOps/MLOps, model evaluation, observability, and monitoring.

- Experience with cloud platforms such as AWS, Azure, or GCP.

- Experience with Docker and Kubernetes.

- Knowledge of AI security, responsible AI, guardrails, and data privacy.

- Experience building enterprise-scale AI platforms and solutions.

- Strong understanding of software engineering principles, APIs, microservices, and distributed systems.