Technical Project Manager - Data & AI

Location: Pune, India

Job Requirements Technical Project Manager - Data & AI End-to-end delivery | Data engineering and AI fluency | Client and stakeholder leadership Function Data & AI Delivery Level Project Manager / Senior Project Manager Experience 12+ years overall, including 3+ years managing Data, Analytics or AI programs Work model Client-facing / Hybrid; may include distributed global teams Role Purpose The Technical Project Manager will lead customer-facing Data & AI initiatives from planning through production adoption. The role combines strong project-management discipline with practical technical fluency across data engineering, analytics, machine learning, Generative AI and agentic AI. The manager will translate business outcomes into executable plans, coordinate architects and engineering teams, manage dependencies and delivery risks, and communicate clearly with customer and internal stakeholders. The role is technology-agnostic and must continuously adapt delivery practices to evolving Data & AI platforms, architecture patterns and market trends. Key Roles & Responsibilities Delivery Planning & Execution Own end-to-end delivery across discovery, architecture, engineering, testing, deployment, hypercare and transition to operations. Convert business objectives and solution designs into scope, milestones, releases, backlog, estimates, resource plans and acceptance criteria. Select and apply Agile, Scrum, Kanban or hybrid delivery practices appropriate to the engagement rather than following ceremonies mechanically. Track schedule, effort, budget, quality, dependencies, decisions and benefits; drive corrective actions when delivery deviates from plan. Technical Delivery Management Work confidently with data architects, data engineers, analytics engineers, data scientists, AI/ML engineers, software engineers, QA, DevOps/MLOps and security teams. Understand data pipelines, ETL/ELT, batch and streaming, medallion/lakehouse patterns, warehouses, data modeling, semantic layers, governance, data quality and observability. Understand the delivery lifecycle for ML, GenAI, RAG and agentic AI, including data readiness, experimentation, evaluation, grounding, guardrails, human oversight, deployment and monitoring. Challenge estimates, plans and readiness using evidence; surface technical debt, integration constraints, environment gaps and non-functional requirements early. Coordinate work across modern data and AI ecosystems without limiting delivery to a specific cloud, platform, model or framework. Client & Stakeholder Leadership Act as the primary delivery interface for customer product owners, business leaders, technology teams, vendors and internal leadership. Run governance forums and steering reviews with clear status, decisions required, risks, options and recommendations. Translate technical issues into business impact and communicate trade-offs on scope, timeline, quality, cost and risk. Set realistic expectations, manage change requests and maintain customer confidence through transparent, timely communication. Governance, Risk & Quality Maintain an actionable RAID log, dependency map, decision log, change control and escalation path. Ensure delivery includes security, privacy, data governance, responsible AI, regulatory and architecture-review requirements. Drive quality through definition of ready/done, test strategy, data reconciliation, model/AI evaluation, release gates and production-readiness checks. Ensure AI initiatives define measurable evaluation criteria for quality, groundedness, safety, latency, cost and business outcomes where relevant. Team & Commercial Management Lead distributed, cross-functional teams with clear ownership, priorities, ways of working and escalation discipline. Manage staffing, capacity, onboarding, utilization, vendor dependencies and knowledge continuity with relevant practice teams. Track commercial commitments including effort, forecast, budget, billing milestones, change requests and delivery leakage. Promote reusable assets, automation, continuous improvement and lessons learned across the delivery team. Market Awareness & Continuous Improvement Stay current with developments in cloud data platforms, data products, real-time architectures, semantic layers, GenAI, agentic systems, AI engineering and responsible AI. Assess how new capabilities affect delivery scope, skills, risk, governance and operating models, without pursuing technology for its own sake. Support proposals, estimates, delivery approaches and customer roadmaps with practical implementation insight. Mandatory Skills & Experience 8-12 years of technology experience, including at least 3 years managing Data, Analytics, ML or AI projects. Strong end-to-end project management across scope, schedule, budget, quality, resources, risk, dependencies, change and stakeholder communication. Practical understanding of modern data engineering, cloud data platforms, data modeling, governance, data quality, BI and production operations. Working knowledge of ML, Generative AI, RAG and agentic AI delivery lifecycles, including evaluation, responsible AI and LLMOps/MLOps considerations. Experience managing cross-functional technical teams and coordinating architecture, engineering, testing, security and business workstreams. Strong Agile delivery skills and proficiency with tools such as Jira, Azure DevOps, Confluence or equivalent portfolio and collaboration tools. Ability to read architecture diagrams, understand technical dependencies, question assumptions and communicate implications to non-technical stakeholders. Excellent client communication, executive reporting, facilitation, negotiation, problem solving and decision-making skills. Experience with delivery financials, estimation, capacity planning, forecasting and change control. Preferred Skills & Certifications Experience delivering solutions on one or more modern data/AI ecosystems such as Databricks, Snowflake, Microsoft Fabric, Azure, AWS or Google Cloud. Exposure to data catalog, governance, semantic, BI, orchestration, streaming, MLOps/LLMOps and AI observability capabilities. Experience taking AI or data products from proof of concept through production, adoption and support. PMP, PRINCE2, PMI-ACP, Scrum Master, SAFe or equivalent delivery certification. Relevant cloud, data, analytics or AI foundation certification. Experience in consulting, regulated industries, fixed-price delivery or globally distributed programs. Expected Outcomes Predictable delivery against agreed scope, milestones, quality, cost and acceptance criteria. Transparent governance with timely decisions, risks, dependencies and change control. Technically credible coordination across data and AI workstreams, including production-readiness and responsible AI controls. Strong customer confidence, clear executive communication and measurable business outcomes. Sustainable operations supported by documentation, monitoring, handover and accountable ownership. Education Bachelor's degree in Engineering, Computer Science, Information Systems or a related discipline. A relevant postgraduate qualification is advantageous.