Engineering Manager - AI

Key Responsibilities

• Lead and manage AI/ML/GenAI engineering teams responsible for developing enterprise AI solutions.
• Provide hands-on engineering leadership across architecture, design, development, testing, deployment and production support.
• Manage engineers, technical leads and cross-functional technical teams.
• Drive the complete engineering lifecycle from requirements design development testing deployment production.
• Establish and drive Agile/Scrum practices including sprint planning, backlog refinement, stand-ups, reviews and retrospectives.
• Work closely with Product Managers, Data Scientists, AI/ML Engineers, Architects and business stakeholders.
• Review technical designs, solution approaches and engineering decisions.
• Identify and resolve technical risks, dependencies, resource constraints and engineering bottlenecks.
• Ensure engineering teams follow best practices around coding, testing, CI/CD, DevOps, security, scalability and maintainability.
• Mentor engineers and technical leads and build high-performing engineering teams.
• Participate in hiring, performance management, career development and capability building.
• Drive technical roadmap and engineering strategy for AI initiatives.
• Lead development and implementation of AI/ML, GenAI, LLM, RAG and Agentic AI solutions.
• Ensure AI solutions are production-ready, scalable, reliable and cost-efficient.
• Collaborate with global and senior stakeholders and communicate technical progress, risks and outcomes.
• Drive continuous improvement in engineering processes, productivity and quality.

Required Skills & Experience

• 10+ years of overall IT experience with significant experience in Engineering Management.
• Proven experience managing software/AI/ML engineering teams.
• Strong technical/engineering background, preferably starting as a Software Engineer, AI Engineer, ML Engineer, Data Engineer, Data Scientist or similar technical role.
• Hands-on experience with AI/ML/Generative AI projects.
• Strong understanding of AI/ML engineering lifecycle and production implementation.
• Experience with GenAI, LLMs, RAG, AI Agents or related technologies.
• Strong experience in Agile/Scrum methodologies.
• Experience managing sprint execution, engineering backlogs, priorities and technical dependencies.
• Strong understanding of SDLC, software architecture, APIs, cloud, CI/CD and DevOps.
• Experience with engineering team management, mentoring, hiring and performance management.
• Ability to review and challenge technical designs and guide engineering teams on technical decisions.
• Strong communication, stakeholder management and problem-solving skills.

Good to Have

• Experience with AWS / Azure / GCP.
• Python or other programming experience.
• Databricks / Snowflake / modern data platforms.
• MLOps / LLMOps.
• Azure OpenAI / OpenAI APIs.
• LangChain / LangGraph.
• Vector databases and RAG architectures.
• Agentic AI / multi-agent systems.
• Experience building enterprise-scale AI platforms.
• PMP / Scrum / Agile / Cloud / AI certifications.