Engineering Manager Delivery (Data & AI)

Job Summary

• We are seeking an experienced Engineering Manager Delivery (Data & AI) with 14-20 years of experience in Software Engineering, Data Engineering, Artificial Intelligence, Cloud Technologies, and Enterprise Solution Delivery
• The ideal candidate will lead multiple AI and Data Engineering delivery engagements while providing technical leadership, people management, and client-facing consulting
• The role involves managing cross-functional engineering teams comprising AI Engineers, Data Engineers, Solution Architects, Scrum Masters, and Technical Leads to ensure successful delivery of enterprise-grade AI, Analytics, and Cloud solutions
• The candidate will be responsible for delivery planning, sprint execution, backlog management, project governance, risk mitigation, resource allocation, and ensuring projects are delivered on time, within scope, and aligned with Statements of Work (SOWs)
• Strong expertise in reviewing solution architecture, guiding engineering teams on Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI frameworks, Data Engineering pipelines, MLOps, DevOps, and cloud platforms such as Azure, AWS, or GCP is essential
• The role also requires participation in client discovery workshops, solution discussions, executive stakeholder presentations, delivery reviews, and resolution of technical and delivery challenges
• Additionally, the Engineering Manager will drive engineering excellence, establish delivery best practices, mentor engineering teams, conduct performance reviews, support hiring and technical interviews, monitor delivery KPIs, improve operational governance, and ensure scalable, secure, and production-ready AI solutions
• The ideal candidate should possess exceptional leadership, stakeholder management, communication, analytical thinking, and problem-solving skills, with proven experience managing multiple enterprise AI and Data Engineering projects in consulting, professional services, or high-growth technology organizations

.Responsibilities

• Lead multiple AI and Data Engineering delivery engagements while providing technical leadership, people management, and client-facing consulting.
• Manage cross-functional engineering teams comprising AI Engineers, Data Engineers, Solution Architects, Scrum Masters, and Technical Leads to ensure successful delivery of enterprise-grade AI, Analytics, and Cloud solutions.
• Responsible for delivery planning, sprint execution, backlog management, project governance, risk mitigation, resource allocation, and ensuring projects are delivered on time, within scope, and aligned with Statements of Work (SOWs).
• Strong expertise in reviewing solution architecture, guiding engineering teams on Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI frameworks, Data Engineering pipelines, MLOps, DevOps, and cloud platforms such as Azure, AWS, or GCP is essential.
• The role also requires participation in client discovery workshops, solution discussions, executive stakeholder presentations, delivery reviews, and resolution of technical and delivery challenges.
• Additionally, the Engineering Manager will drive engineering excellence, establish delivery best practices, mentor engineering teams, conduct performance reviews, support hiring and technical interviews, monitor delivery KPIs, improve operational governance, and ensure scalable, secure, and production-ready AI solutions.
• The ideal candidate should possess exceptional leadership, stakeholder management, communication, analytical thinking, and problem-solving skills, with proven experience managing multiple enterprise AI and Data Engineering projects in consulting, professional services, or high-growth technology organizations.

Requirements

• An experienced Engineering Manager Delivery (Data & AI) with 1420 years of experience in Software Engineering, Data Engineering, Artificial Intelligence, Cloud Technologies, and Enterprise Solution Delivery.

Key Skills

• Generative AI
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Agentic AI frameworks
• Data Engineering pipelines
• MLOps
• Azure
• AWS
• GCP