Program Director (DFS)

Job Summary

Job Description: AI Project Manager

Location: India

About the Role

We are seeking an experienced AI Project Manager to lead the implementation of AI and Generative AI projects, from pre-sales solutioning through project execution and customer success. This role requires someone who can engage credibly with AI Solution Architects and data science teams on solution design, while also excelling at stakeholder management, project governance, and driving measurable business outcomes for customers.

The ideal candidate combines strong project management discipline with a working understanding of AI/ML and Generative AI concepts, enabling them to manage the unique risks, iterative nature, and cross-functional complexity of AI project delivery.

Key Responsibilities

Key Responsibilities AI Project Implementation: Lead end-to-end delivery of AI/ML and Generative AI projects, including use case discovery, model development, MLOps/LLMOps pipelines, integration, and production deployment. Manage iterative, experimentation-driven workstreams (data preparation, model training/fine-tuning, prompt engineering, evaluation) alongside traditional delivery milestones. Coordinate cross-functional teams of data scientists, ML engineers, AI Solution Architects, and business analysts to ensure aligned, on-track delivery. Oversee model validation, responsible AI/governance checks, and production readiness prior to go-live. Pre-Sales Support: Partner with sales and pre-sales teams to scope, estimate, and structure AI engagements, including feasibility assessments and proof-of-concept (POC) planning. Support RFP/RFI responses, SOWs, and proposal development with realistic timelines, staffing plans, and phased delivery approaches (POC → pilot → scale). Translate customer business problems into high-level AI solution narratives in collaboration with AI Solution Architects. Participate in client-facing pre-sales discussions to build credibility and confidence in AI delivery capability. Stakeholder Collaboration: Act as the primary liaison between customer stakeholders, internal delivery teams, AI Solution Architects, and executive sponsors. Facilitate discovery workshops, use-case prioritization sessions, and solution design reviews. Manage expectations across technical and non-technical stakeholders, ensuring alignment on scope, data readiness, timelines, and success metrics. Provide regular status reporting, steering committee updates, and executive-level communication. Project Execution & Governance: Own project scope, work breakdown structures, resource plans, budgets, and delivery milestones using Agile, Waterfall, or hybrid methodologies suited to AI project cycles. Proactively manage risks, issues, dependencies, and change requests, including risks specific to AI projects (data quality, model performance, bias/fairness, compliance). Track and report on project financials (budget vs. actuals), resource utilization, and delivery KPIs. Ensure quality assurance, responsible AI practices, and governance standards are met throughout delivery. Interface & Lead Discussions with AI Solution Architects: Serve as the connective tissue between AI Solution Architects and business/customer stakeholders during solution design. Lead technical solutioning discussions, ensuring AI architecture and model approach align with project scope, budget, and timeline constraints. Facilitate architecture/solution review boards, design walkthroughs, and technical decision logs. Ensure AI solution designs are translated into actionable, sequenced project plans with clear milestones and success criteria. Customer Satisfaction & Relationship Management: Own the overall customer experience for assigned engagements, driving high CSAT/NPS outcomes and demonstrable business value from AI initiatives. Proactively identify and resolve delivery risks before they impact customer satisfaction. Conduct regular business reviews, share value/ROI metrics, and gather feedback to identify opportunities for account growth. Act as an escalation point for critical delivery issues, ensuring swift resolution and transparent communication. Contribution to AI Project Management Practice: Contribute to the build-out of internal AI delivery methodologies, templates, playbooks, and best practices tailored to AI/ML project lifecycles. Mentor and coach junior project managers and delivery leads o

Skill Requirements

Required Qualifications

Experience

• 6+ years of project management experience, with at least 3+ years managing AI/ML or Generative AI projects.


• Proven experience in pre-sales support, solution scoping, and proposal/SOW development for AI engagements.


• Demonstrated experience managing cross-functional teams including data scientists, ML engineers, and AI architects.


• Experience navigating the iterative, experimentation-heavy nature of AI project delivery, including POC-to-production journeys.

Skills & Competencies

• Strong working knowledge of AI/ML and Generative AI concepts (model training, fine-tuning, RAG, prompt engineering, MLOps/LLMOps) sufficient to engage credibly in architecture discussions.


• Awareness of responsible AI principles — bias, fairness, explainability, data privacy, and regulatory considerations.


• Excellent stakeholder management, executive communication, and presentation skills.


• Strong command of project management methodologies (Agile/Scrum, Waterfall, SAFe) and tools (Jira, MS Project, Smartsheet, Asana, or similar).


• Financial acumen — budget planning, tracking, and forecasting for technical projects.


• Strong risk management, negotiation, and conflict-resolution skills.


• Ability to translate complex AI/technical concepts into business-friendly language and vice versa.

Other Requirements

Mandatory Certifications

• PMP (Project Management Professional) or PRINCE2 certification (mandatory)


• At least one industry-recognized AI certification (mandatory), such as:




• Google Cloud Professional Machine Learning Engineer or Google Cloud Generative AI Leader


• AWS Certified Machine Learning – Specialty


• Microsoft Certified: Azure AI Engineer Associate


• NVIDIA Certified Associate / Professional (AI-related tracks)




• Preferred additional certifications:




• Certified ScrumMaster (CSM) / PMI-ACP


• Data Science / Deep Learning certifications (e.g., DeepLearning.AI)


• ITIL Foundation

Education

• Bachelor's degree in Computer Science, Data Science, Engineering, or related field (Master's degree preferred).