The Delivery Team Leader leads a multidisciplinary engineering team responsible for delivering cloud-native software, AI, and agent-based solutions. The role combines people leadership, end-to-end delivery ownership, hands-on technical leadership, and engineering design responsibility.
A core part of the role is leading the team’s transformation into an AI-Driven Delivery Team—embedding AI and Agentic capabilities throughout the SDLC while maintaining strong engineering foundations, architectural quality, security, and operational excellence across multiple concurrent initiatives.
Key Responsibilities
People Management & Leadership:
Direct management of ~15 employees across diverse engineering disciplines. Responsible for mentoring, personal and professional development, building work plans, capacity management, and cultivating a high-performing engineering culture focused on continuous learning, ownership, and operational excellence.
Delivery Management:
End-to-end responsibility for delivery, product quality, execution, and meeting deadlines across multiple concurrent cloud-native, AI-driven, and agent-based projects. Establish engineering standards and measurable delivery practices that improve speed, quality, consistency, and predictability.
Design & Architecture:
Own the end-to-end engineering design of delivered solutions, from solution design and technical decomposition through implementation and production readiness. Define scalable design principles and influence key architectural decisions to ensure consistency, security, maintainability, reliability, and alignment with enterprise architecture and cloud best practices.
AI-Driven Delivery Transformation:
Lead the team’s evolution into an AI-Driven Delivery Team by embedding AI models, AI Agents, and AI-native engineering practices throughout the SDLC—from discovery, requirements, planning, and solution design through development, testing, CI/CD, deployment, operations, and continuous improvement.
Define and implement agent-enabled workflows that automate and augment engineering activities while maintaining appropriate human oversight, quality controls, security, and governance. Drive adoption of modern AI development tools and harnesses, establish reusable practices and standards, and continuously measure their impact on engineering productivity, delivery speed, and quality—positioning the team for the next generation of software delivery.

Management Experience: 3+ years of engineering/development management experience, with strong leadership skills, a people-first approach, and data-driven decision-making. Technical Experience (Hands-on): 5+ years of hands-on Full Stack development experience, with a proven track record of delivering complex software products and AI-enabled solutions from concept to production. Experience designing or developing autonomous AI Agents is a strong advantage. Architecture & Cloud Infrastructure: Deep understanding of software architecture, distributed systems, system design, and cloud-native engineering, alongside extensive experience with leading cloud providers (AWS, GCP, or Azure). AI & Agentic Engineering Expertise: Strong knowledge of modern AI and LLM models, their capabilities, limitations, and enterprise integration patterns, together with proven hands-on experience designing and building AI Agents. Experience with agent frameworks, orchestration approaches, evaluation techniques, and AI development harnesses/tooling used to design, build, test, evaluate, and integrate agentic solutions into enterprise software and SDLC workflows. AI-Native SDLC: Practical understanding of how AI and Agents can be embedded across planning, design, development, testing, deployment, and operations to create scalable AI-native engineering workflows. Ability to define human-in-the-loop controls, engineering guardrails, reusable patterns, and measurable practices for responsible adoption at scale. DevOps & Automation: Vast and in-depth experience with CI/CD processes, automation, DevSecOps practices, observability, and production operations, with the ability to integrate AI-enabled capabilities into modern engineering toolchains. Personal & Business Skills: Strategic leadership capabilities, excellent communication skills, ability to thrive under pressure and manage multiple projects simultaneously, along with a product and service-oriented mindset.