Description

Architect and scale Agentic AI systems with multi-step reasoning capabilities while defining reference architectures for enterprise integration.

This role is hybrid.

Responsibilities

• Design AI-driven frameworks for application modernization and migration, ensuring seamless integration into existing enterprise systems.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines, including vector databases and memory systems.
• Evaluate and refine the LLM stack, covering model selection, orchestration, and observability tools.
• Implement advanced prompting strategies such as few-shot learning, chain-of-thought, and chunking techniques.
• Lead and mentor a high-performing GenAI team while driving the adoption of AI-assisted coding tools like GitHub Copilot and Claude Code.

Required Skills

• 10+ years of experience in AI/ML Engineering with a focus on Agentic AI and LLM systems.
• Strong expertise in Kubernetes for container-based hosting and cloud-native architectures.
• Hands-on experience with major cloud platforms: Azure, AWS, or GCP.
• Proficiency with LangGraph and LangMem for building complex LLM workflows.
• Deep understanding of AI safety, data privacy, governance, and compliance standards.
• Experience integrating hybrid ML and LLM systems into production environments.
• Practical knowledge of RAG architectures and vector database implementations.
• Proficiency with GitHub Copilot.

Preferred Skills

• Proven track record in enterprise application migration projects.
• Leadership experience in managing and scaling AI engineering teams.

Key Skills

Education

Any Gradute