AI Technical Lead – GenAI & Agentic AI

Summary

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.

Essential Job Duties And Responsibilities

• AI Strategy & Solution Architecture
  - 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.

• Agentic AI & Intelligent Systems
  - 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.

• AI Application Development
  - Lead development of AI-powered applications utilizing LLMs, foundation models, and enterprise knowledge sources.
  - Design and implement solutions leveraging: Model Context Protocol (MCP), External APIs, Enterprise data platforms, Knowledge management systems, Business applications.
  - Guide engineering teams in implementing production-grade AI architectures and services.

• AI Evaluation, Observability & Governance
  - Define and implement AI evaluation frameworks covering: Response quality, Groundedness, Hallucination detection, Agent performance, Safety and reliability.
  - Support responsible AI, governance, compliance, and model risk management practices.
  - Develop metrics and measurement frameworks to assess business and technical outcomes.

• Cloud & Platform Engineering
  - 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.
  - Ensure solutions meet enterprise requirements for availability, security, performance, and cost efficiency.

• AI-Assisted Engineering & Productivity
  - Champion adoption of AI-assisted development platforms including: Claude Code, GitHub Copilot, CursorEquivalent AI engineering tools.
  - Establish best practices for AI-enabled software development and engineering productivity.
  - Identify opportunities to accelerate delivery through AI-assisted workflows and automation.

• Customer Engagement & Technical Consulting
  - 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.

Other Job Duties And Responsibilities

• Performs other related duties as assigned.

• Complies with all company policies and procedures.

• Maintains regular and punctual attendance.

Qualifications

• Required Technical Skills:

  • 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

• Preferred Qualifications:

  • 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