About the role
We are seeking an experienced Agentic AI Architect to lead the design, development, and deployment of next-generation AI solutions powered by autonomous agents, multi-agent systems, LLMs, RAG, Knowledge Graphs, and AI orchestration frameworks. The ideal candidate will possess a strong blend of AI/ML expertise, solution architecture skills, and hands-on experience in delivering enterprise-scale AI initiatives.
Responsibilities:
- Design and architect agentic AI solutions leveraging LLMs, autonomous agents, multi-agent systems, and AI orchestration frameworks.
- Lead the development of AI-powered workflows using platforms such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or similar frameworks.
- Define agent planning, reasoning, memory, tool usage, and collaboration strategies for complex enterprise use cases.
- Integrate RAG pipelines, vector databases, Knowledge Graphs, APIs, and enterprise data sources into agent ecosystems.
- Provide technical leadership, architecture guidance, and best practices for scalable, secure, and responsible AI implementations.
- Collaborate with business stakeholders to identify high-value opportunities for AI-driven automation and decision intelligence.
- Establish governance, evaluation, observability, and performance monitoring frameworks for AI agents.
- Mentor engineering teams and contribute to AI capability development across the organization.
Requirements:
- Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field.
- 12-20 years of experience in Web development, AI/ML, data science, software engineering, or solution architecture.
- 3+ years of hands-on experience with Generative AI and LLM-based solutions.
- Strong experience with Agentic AI frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or OpenAI Agents SDK.
- Expertise in RAG, vector databases, embeddings, prompt engineering, and AI orchestration.
- Experience with cloud platforms such as Azure AI, AWS Bedrock, or Google Vertex AI.
- Proficiency in Python, APIs, MLOps, and enterprise application integration.
- Strong understanding of responsible AI, security, governance, and model evaluation.
- Experience with GraphRAG, Knowledge Graphs, Neo4j, and enterprise search solutions.
- Exposure to AI copilots, workflow automation, and intelligent process automation.
- Relevant certifications in Azure AI, AWS AI/ML, or Generative AI technologies.