A Delivery Manager at EPAM combines several roles in one. You are a hands-on technologist who can dive into engineering details down to code, and a former Technical Lead who still designs technical solutions. You bring a Project Manager's full skill set in delivery governance, risk and stakeholder management, and you stay close to the customers we deliver for, building lasting relationships and spotting opportunities to grow the account. You also lead the shift to AI-driven delivery, bringing AI into how your teams build software and building AI agents and skills yourself. Delivery Managers typically start with end-to-end delivery of one or two teams and take on larger setups as their experience grows. We welcome candidates with a solid track record at either stage. You typically report to a member of the senior leadership team who owns the wider program, portfolio or account, and run day-to-day delivery independently. You are a curious, proactive problem solver who adds value well beyond coordination. Responsibilities Lead end-to-end delivery of your teams (a project, sub-project or stream), owning scope, timelines, quality and outcomes Own your team's staffing and composition: define the roles and skills the project needs, interview candidates, and assess their skills and seniority Set up or take over the delivery environment, both technically (tooling, environments, CI/CD) and process-wise (ways of working, team ceremonies, roles and responsibilities), and adapt it to the project's needs Plan and estimate effort, schedule delivery and track milestones, working with senior leadership on budgets Establish team-level delivery governance: delivery reviews, health checks, reporting and escalation paths, so the project's state is clear at every SDLC phase Drive data-driven delivery: define and track metrics across all SDLC stages and delivery aspects (scope, schedule, quality, productivity, team health), tailor them to the commercial model and SDLC, and use them to steer decisions and improvements Identify, assess and mitigate scope, schedule, budget and resource risks; run risk reviews and escalate early when needed Act as a problem solver on the project, focused on delivering value to the customer Make software architecture decisions within your teams' scope and justify them against functional and non-functional requirements Translate architecture into detailed implementation plans and suggest improvements to the overall solution design Dive deep into problem areas down to the code level and support the team in resolving them Design the full CI/CD process and continuously remove pipeline inefficiencies Design the quality process across all SDLC stages: coding standards, code review, static and dynamic analysis, refactoring and testing Choose and adapt the delivery methodology (Scrum, Kanban or other) to the project's needs Drive adoption of an AI-powered SDLC in your teams, from coding and testing to project management, and measure its impact Build AI agents and skills hands-on to automate team workflows and raise delivery efficiency Evaluate and select GenAI tools and platforms, and explain their benefits to the team and stakeholders Contribute to delivery of AI-based solutions and to EPAM's GenAI offerings in client and presales conversations Own direct communication with client stakeholders on technical and delivery topics; handle conflicts and take responsibility Quickly learn the client's domain, internal processes and delivery practices, and align the team's ways of working with them Lead your teams by example: coach, give feedback, manage performance and build a learning, inclusive environment Contribute to presales through estimations, architecture reviews and documentation Share delivery best practices and knowledge back with the wider EPAM community Requirements 5+ years in IT with a strong software engineering background, including 2+ years as a Lead Developer, Team Lead or Delivery Lead Proven track record of managing delivery for teams of 5+ FTEs Ability to read and understand code and architecture of a full-stack solution, and to design and deliver one Working knowledge of architectural styles (n-tier, SOA, event-driven, microservices) and design notations such as UML Ability to design for quality attributes such as performance, scalability, security and maintainability, and to justify trade-offs Advanced, hands-on CI/CD experience: designing pipelines from scratch and optimizing existing ones Hands-on experience with at least one major cloud platform (Google Cloud, AWS or Azure); cloud certifications are a plus Advanced code quality practice: establishing code review, static analysis and coding standards for a team, whether new or inherited Hands-on experience driving AI-assisted SDLC adoption in a team Hands-on experience building AI agents and skills (for example with LLM APIs and agent frameworks) Solid project management practice: scope, estimation, planning, risk, quality and change management Hands-on experience setting up delivery metrics across all SDLC stages and delivery aspects, and adjusting the metric set to the commercial model (T&M, Fixed Fee, Managed Service) and SDLC approach Experience establishing team-level delivery governance: cadences, reviews, reporting and escalation Practical experience with Agile frameworks (Scrum, Kanban) and the ability to choose and tune a methodology for the project Understanding of testing fundamentals: test levels, test types and test activities Experience with direct client communication on technical topics and with resolving conflicts, ideally in international, multicultural delivery environments People leadership: coaching, giving feedback, managing performance and interviewing candidates Strong ownership, adaptability and clear verbal and written communication, with the confidence to challenge existing approaches constructively English at B2 level or higher Nice to have Experience delivering AI-based solutions, such as LLM-powered applications, chatbots or recommendation engines Awareness of legal, ethical and compliance aspects of GenAI: privacy, data security, IP and bias Familiarity with a scaled agile framework (SAFe, LeSS or Nexus) Basic understanding of engagement models (Staff Augmentation, Managed Capacity, Managed Service) Basic understanding of project PL
