AI Technical Lead - GenAI & Agentic AI

Role Description

The AI Technical Lead is responsible for leading the design, architecture, development, and deployment of enterprise-scale Generative AI and Agentic AI solutions that drive business transformation and innovation. This role combines deep technical expertise in Large Language Models (LLMs), Agentic AI systems, cloud-native AI platforms, and AI engineering best practices with strong customer engagement and technical leadership capabilities.

• Lead the end-to-end design and delivery of Generative AI and Agentic AI solutions.
• Define reference architectures, implementation patterns, and engineering standards for enterprise AI applications.
• Evaluate emerging AI technologies and frameworks and recommend adoption strategies.
• Ensure AI solutions align with enterprise architecture, security, governance, and scalability requirements.
• Architect and implement Agentic AI systems, including:
  • Multi-agent workflows
  • Agent orchestration patterns
  • Memory architectures
  • Tool integration frameworks
  • Retrieval-Augmented Generation (RAG) solutions
• Design AI agents capable of interacting with enterprise systems, APIs, business processes, and knowledge repositories.
• Establish reusable frameworks for autonomous and semi-autonomous agent execution.
• Lead development of AI-powered applications utilizing LLMs, foundation models, and enterprise knowledge sources.
• Guide engineering teams in implementing production-grade AI architectures and services.
• Define and implement AI evaluation frameworks covering:
  • Response quality
  • Groundedness
  • Hallucination detection
  • Agent performance
  • Safety and reliability
• Establish AI monitoring, observability, and operational performance standards.
• Support responsible AI, governance, compliance, and model risk management practices.
• Develop metrics and measurement frameworks to assess business and technical outcomes.
• Design, deploy, and optimize AI solutions across Azure, AWS, and Google Cloud environments.
• Collaborate with platform teams to establish scalable and secure AI infrastructure.
• Drive adoption of cloud-native architectures, LLMOps, MLOps, and automation practices.
• Champion adoption of AI-assisted development platforms including:
  • Claude Code
  • GitHub Copilot
  • Cursor
  • Equivalent AI engineering tools
• Work directly with customers to understand business challenges and identify AI-driven opportunities.
• Translate business requirements into scalable technical architectures and implementation plans.
• Lead technical workshops, architecture reviews, proofs of concept, and solution demonstrations.
• Serve as a trusted advisor on AI strategy, implementation, and operationalization.

Qualifications

• Advanced Python development
• Large Language Models (LLMs)
• Generative AI application development
• Retrieval-Augmented Generation (RAG)
• Agentic AI architectures
• Multi-agent systems
• Model Context Protocol (MCP)
• Tool-calling and agent orchestration frameworks
• AI evaluation and observability platforms
• Cloud platforms (Azure, AWS, Google Cloud)
• API design and integration
• Enterprise application architecture

Requirements

• Experience with LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar agent orchestration frameworks.
• Experience implementing AI governance, responsible AI, and model risk management frameworks.
• Familiarity with MLOps, LLMOps, CI/CD pipelines, and cloud-native deployment models.
• Experience leading enterprise-scale AI transformation initiatives.
• Banking & Financial Services industry experience, including exposure to customer servicing, operations, risk, underwriting, fraud, claims, or compliance use cases.

Education and/or Experience

• Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or a related field.
• 10+ years of experience in Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or related technical disciplines.
• Demonstrated experience leading complex AI solution delivery programs from concept through production deployment.
• Proven experience working directly with business stakeholders and customers in consulting or solution delivery environments.