Engineering Manager - AI

We are looking for an Engineering Manager to lead a team building agentic AI and GenAI systems for enterprise clients. This is a player-coach role: you will manage engineers and their delivery while staying hands-on enough to review architecture, unblock hard technical problems, and hold the line on quality and craft. You will own team output, technical direction, and delivery predictability for your pod.

Responsibilities:

• Manage and grow a team of AI/ML and software engineers building agentic AI and GenAI systems in production.
• Own technical direction for the team: architecture choices, design patterns, and code quality for agent workflows, RAG pipelines, and LLM integrations.
• Plan and track delivery: break down roadmaps into sprints, manage timelines, and remove blockers to keep the team predictable and on schedule.
• Stay hands-on: review code and designs, pair on hard problems, and step in on critical issues when needed.
• Own team hiring, onboarding, performance management, and career growth for engineers on the team.
• Partner with architects, product managers, and delivery leads to translate client requirements into engineering plans.
• Own the engineering quality bar: testing, CI/CD, code review standards, and production reliability for AI systems.
• Drive adoption of evaluation, guardrails, and observability practices for agentic AI and LLM-based systems.
• Represent the team's technical progress and risks to senior leadership and, where needed, to clients.

Requirements:

• 10+ years of hands-on software engineering experience, with a strong, unbroken technical track record.
• Demonstrated experience managing or leading engineering teams, including hiring, performance management, and career development.
• Hands-on experience building agentic AI systems, including at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel).
• Practical experience with GenAI/LLM systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers.
• Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices.
• Experience managing delivery: sprint planning, estimation, and predictable execution against timelines.
• Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases.
• Understanding of LLM safety and reliability practices: guardrails, prompt-injection defenses, evaluation, and observability.

Preferred Qualifications:

• Experience managing distributed or hybrid engineering teams.
• Experience integrating enterprise systems such as Salesforce, Microsoft Dynamics, ServiceNow, or SAP.
• Experience with fine-tuning, model distillation, or self-hosted/open-weight model deployment.
• Experience working with stakeholders from the Middle East, ideally government or semi-government entities.
• Certifications: AWS ML Specialty, AWS Solutions Architect Professional, or Azure Solutions Architect Expert.