Technical Product / Project Lead

Technical Product / Project Lead

Key Responsibilities

1) Program & Delivery Ownership

• Own end-to-end delivery for AI/automation projects — from backlog intake through pilot and production rollout.
• Drive sprint planning, backlog refinement, and milestone tracking in Azure DevOps (ADO).
• Maintain visibility into task status, risks, and dependencies across the offshore development team.
• Ensure delivery adherence to AI CoE governance, security, and compliance standards.

2) Technical & Product Translation

• Translate business requirements (captured via intake and AI CoE validation) into technical scope, user stories, and acceptance criteria.
• Partner with data scientists, engineers, and SMEs to define architecture, data sources, and integration needs.
• Review functional and technical designs to ensure they align with client needs.

3) Stakeholder & Communication Management

• Serve as the day-to-day liaison between U.S. AI CoE, Steering Committee, and IDC teams.
• Communicate progress, risks, and blockers through dashboards and structured updates.
• Foster trust and transparency with practice leads and client-facing delivery teams.

4) Process & Quality Governance

• Ensure every initiative follows the standard process (Intake > Prioritization > Pilot > Scale > Monitoring).
• Maintain documentation, including Business Requirement Summaries, Technical Design Docs, and Pilot Reports.
• Conduct quality checks on backlog readiness, sprint output, and release deliverables.

5) People & Collaboration

• Coordinate cross-functional collaboration between developers, data engineers, ML engineers, and QA.
• Support onboarding and knowledge sharing as new team members join the AI CoE team.
• Drive a culture of accountability, continuous improvement, and delivery excellence.

Required Skills & Experience

1) Technical Skills

• 8–12 years of experience managing technology or AI/automation projects (preferably with hybrid onshore-offshore models).
• Proven experience with Azure DevOps (ADO) or Jira for backlog and sprint management.
• Working knowledge of AI/ML lifecycle (data prep, model training, validation, deployment, monitoring).
• Familiarity with Azure AI services, OpenAI APIs, and cloud integration concepts.
• Basic understanding of data engineering pipelines (ETL, SQL, Python) and application integration frameworks.
• Strong grasp of governance, compliance, and risk management principles for AI projects.

2) Product & Delivery Skills

• Ability to translate business use cases into structured technical requirements.
• Experience defining KPIs, success criteria, and ROI metrics for pilots and scale solutions.
• Excellent backlog grooming, prioritization, and dependency management skills.
• Strong communication and stakeholder management with U.S.-based leadership teams.

3) Soft Skills

• Self-driven, structured, and detail-oriented with a delivery-first mindset.
• Exceptional written and verbal communication skills.
• Proven ability to manage ambiguity and drive clarity in complex environments.
• Comfortable leading daily stand-ups, retrospectives, and technical deep dives.

4) Preferred Qualifications

• PMP, CSM, or SAFe certification preferred.
• Experience working in or building an AI Center of Excellence or data-driven product organization.
• Prior experience with U.S. clients in consulting or managed service environments.

5) Success Indicators

• Projects delivered on time, within scope, and aligned with defined outcomes.
• Clear traceability from intake through execution in ADO.
• Positive feedback from U.S. CoE and practice leaders on communication and delivery quality.
• Reusable project documentation and accelerators created for future AI initiatives.