Job Summary
Project Manager – AI Programmes
ROLE OVERVIEW
We are seeking a seasoned Project Manager to lead the delivery of AI/ML and data-driven transformation programmes within our AI Practice. You will own the full programme lifecycle — from initiation and planning through execution, monitoring, and closure — ensuring on-time, on-budget, and in-scope delivery while maintaining the highest standards of quality and client satisfaction.
AI programmes are inherently iterative and unpredictable. The ideal candidate brings strong classical PM discipline alongside the flexibility and technical curiosity to manage data science teams, ML experimentation cycles, model deployment pipelines, and change management in a single integrated programme.
Key Responsibilities
KEY RESPONSIBILITIES
Programme & Project Planning
- Define project scope, objectives, milestones, and success criteria in collaboration with clients and internal delivery teams
- Build and maintain detailed project plans covering data acquisition, model development, integration, testing, and deployment phases
- Develop resource plans, effort estimates, and budget forecasts; track actuals vs. plan
- Identify interdependencies across workstreams (data engineering, AI/ML, cloud, change management) and manage critical path
Delivery Execution & Governance
- Drive day-to-day delivery across multi-disciplinary AI teams: data scientists, ML engineers, cloud architects, BAs, and change leads
- Run Agile/Scrum or hybrid delivery ceremonies — sprint planning, standups, retrospectives, and release management
- Maintain programme governance artefacts: RAID logs, status reports, decision logs, and steering committee packs
- Proactively identify, escalate, and mitigate risks and issues before they impact delivery or client trust
Stakeholder & Client Management
- Serve as the primary point of contact for client programme sponsors, delivery leads, and IT teams
- Run steering committees and executive status reviews with confidence; communicate progress, risks, and decisions clearly
- Manage scope change requests through formal change control; assess impact on timeline, budget, and quality
- Maintain strong client satisfaction (CSAT) scores throughout delivery and post-go-live
Financial & Commercial Management
- Own programme P&L: track revenue recognition, invoicing milestones, and margin performance
- Manage resource utilisation and flag over/under-allocation proactively
- Support contract renewals, SOW amendments, and commercial discussions with client procurement
AI-Specific Delivery Considerations
- Understand AI/ML development cycles: experimentation, model training, evaluation, and iterative improvement loops
- Coordinate model deployment activities: MLOps pipeline setup, UAT, hypercare, and handover to client operations
- Ensure data governance, privacy compliance (GDPR, DPDP), and responsible AI guardrails are embedded in delivery
- Manage expectations around AI project timelines, data readiness, and model performance variability
Quality & Continuous Improvement
- Enforce project quality standards, delivery frameworks (HCLTech DX Methodology, PMI, SAFe), and best practices
- Conduct post-implementation reviews and capture lessons learned for practice-wide improvement
- Champion adoption of programme tooling: Jira, MS Project, Confluence, Power BI dashboards
Skill Requirements
REQUIRED QUALIFICATIONS & EXPERIENCE
Experience
- 10 – 15 years of total experience; 3+ years managing AI/ML, data, or analytics programmes end-to-end
- Proven delivery of complex technology programmes with budgets exceeding $1M and teams of 15+ people
- Prior experience in a technology services, consulting, or systems integration context
- Demonstrable track record of strong client satisfaction and on-budget/on-time delivery
Technical & Domain Knowledge
- Solid understanding of the AI/ML development lifecycle: data preparation, model training, evaluation, deployment, and monitoring
- Familiarity with Generative AI and LLM programme delivery: prompt engineering cycles, RAG pipeline deployment, model governance
- Exposure to cloud platforms (AWS, Azure, GCP) in a programme delivery context
- Comfortable engaging with technical artefacts: solution architectures, data flow diagrams, model cards, and MLOps pipelines
- Understanding of data governance, privacy regulations, and responsible AI frameworks
Project Management Skills
- Mastery of Agile (Scrum/SAFe), Waterfall, and hybrid delivery methodologies
- Strong RAID management, financial tracking, and executive reporting skills
- Proficient with PM tools: Jira, MS Project, Confluence, Smartsheet, or equivalent
- Excellent written and verbal communication — able to tailor messages from development teams to C-suite
Education & Certifications
- Bachelor's degree in Engineering, Computer Science, Business, or related field (required)
- PMP (PMI) or PRINCE2 Practitioner certification (required or in progress)
- Agile certifications (CSM, SAFe PM/PO, PMI-ACP) are highly preferred
- Cloud or AI awareness certifications (Azure Fundamentals, AWS Cloud Practitioner) are a plus