Job Summary

We are seeking an experienced Staff Project Manager, AI Solutions to lead the delivery of AI-powered product launches and Go-To-Market (GTM) initiatives within a fast-paced technology or system integrator environment.

This role is accountable for end-to-end GTM project outcomes including scope, schedule, cross-functional readiness, and commercial launch success. The Project Manager is expected to operate independently in complex, multi-stakeholder environments (engineering, product, sales, marketing, legal), driving execution across distributed teams and ensuring launch commitments are met through facilitation and influence, not authority.

The role involves managing internal AI engineering teams, product managers, revenue operations, and multiple GTM partners, while maintaining strong governance, stakeholder alignment, and tight control over launch scope and risks. Exceptional soft skills in facilitation, negotiation, and ambiguity management are as critical as technical fluency in AI/ML.

Key Responsibilities

Project Delivery (GTM & AI Solution Launch)

• Lead end-to-end delivery of AI solution GTM projects, including new feature launches, beta programs, pricing model rollouts, and sales enablement.

• Drive delivery to meet committed launch dates, sales readiness milestones, quality thresholds (e.g., model accuracy), and commercial targets.

• Develop and manage detailed GTM project plans, including engineering handoffs, legal/ compliance reviews (data privacy, IP), marketing asset creation, and sales training.

• Manage project financials including budget tracking for AI model training costs, inference infrastructure, and GTM campaign spend.

• Ensure delivery aligns with product roadmap commitments while managing the commercial impact of scope changes, model performance slippage, or regulatory delays.

Stakeholder & Client Management

• Serve as the primary facilitator between technical teams (ML engineers, data scientists) and GTM teams (sales, marketing, legal, support).

• Lead and facilitate governance forums including Launch Readiness Reviews, Cross-functional Working Groups, and Executive Steering Committees.

• Provide structured and transparent reporting on project status, launch blockers, model performance risks, and sales adoption metrics.

• Engage senior stakeholders (VP of Sales, Chief Product Officer, Legal Head) effectively to drive decisions, resolve resource conflicts, and maintain launch momentum.

Vendor & Partner Management

• Manage delivery involving external AI model providers (e.g., OpenAI, Anthropic), cloud infrastructure vendors (AWS, Azure, GCP), and offshore development teams.

• Hold vendors accountable for API reliability, latency SLAs, data processing compliance, and model versioning timelines through structured governance.

• Proactively manage and resolve vendor-related delivery risks such as rate limiting, cost overruns, or deprecation of external model endpoints.

Governance & Risk Management

• Maintain and enforce project governance artifacts including GTM RAID logs (Risks, Assumptions, Issues, Dependencies), launch checklists, and milestone trackers.

• Proactively identify, assess, and manage AI-specific risks: model drift, bias, hallucination rates (for GenAI), inference cost escalation, and data privacy exposure.

• Drive structured change management processes including impact assessment of scope changes (e.g., adding a new use case mid-sprint), effort estimation, and formal approvals.

• Ensure all scope changes are controlled, tracked, and aligned with product commercial agreements and launch timelines.

Testing & Deployment (AI & GTM Readiness)

• Oversee and govern AI model testing activities including offline evaluation (precision/recall), A/B testing, user acceptance testing (UAT) with pilot customers, and red-teaming for safety.

• Work closely with QA and ML engineers to ensure test planning, model performance thresholds, and defect (e.g., hallucination) management are properly managed.

• Drive deployment planning for both technical rollout (API endpoint, feature flag) and commercial rollout (pricing page update, sales collateral release).

• Ensure structured transition to Day 2 operations including model monitoring dashboards, customer support knowledge base, sales playbooks, and SLA alignment.

Requirements

Experience

• Minimum 5–8 years of experience in IT or product project management, with at least 2 years specifically on AI/ML, data science, or intelligent automation products.

• Proven experience delivering both AI feature development and GTM launch projects (e.g., bringing a new AI assistant or predictive model to market).

• Experience operating within a System Integrator (SI) or high-growth product/SaaS environment with direct client or internal GTM stakeholder exposure.

• Strong experience managing distributed delivery models including onshore product teams and offshore engineering or data labeling partners.

• Experience managing multi-vendor AI ecosystems involving model providers, cloud platforms, and data annotation services.

• Experience managing client-facing or internal executive engagements involving senior stakeholders from sales, legal, and product.

• Experience facilitating GTM projects such as sales enablement, pricing committee approvals, or beta customer onboarding.

Technical Exposure

• Good understanding of AI/ML lifecycle including data preparation, model training, evaluation, inference, and monitoring.

• Understanding of API integrations, data flows, model versioning, and system interoperability between AI services and downstream business applications.

• Ability to engage effectively with ML engineers, data scientists, solution architects, and product managers.

• Familiarity with cloud AI platforms such as AWS SageMaker, Azure ML, Google Vertex AI, or equivalent (mandatory advantage).

• Exposure to Generative AI concepts (LLMs, prompt engineering, RAG, fine-tuning, embedding) is a strong plus.

• Familiarity with modern MLOps practices (model registry, drift detection, feature store) is a plus.

Certifications

• PMP, CITPM, PRINCE2, or equivalent project management certifications (mandatory).

• Agile certifications such as CSM, PSM, or SAFe (preferred).

• AI/ML specific certifications (e.g., AWS Certified Machine Learning, Azure AI Engineer, or Stanford ML) are a plus but not mandatory.

Key Competencies

• Strong ownership mindset with accountability for end-to-end GTM delivery outcomes

• Ability to operate independently in complex, ambiguous, fast-paced AI environments

• Exceptional facilitation and negotiation skills – can drive alignment across engineering and GTM without direct authority

• Strong GTM and AI solution project management capability – balancing technical depth with commercial readiness

• Effective vendor and partner management with ability to enforce accountability on model performance SLAs

• Strong risk, issue, and change management discipline – especially for AI-specific risks (drift, bias, cost)

• Excellent stakeholder management and communication skills at senior executive level

• Ability to manage distributed technical and GTM teams across time zones

• Strong technical appreciation to engage ML engineers meaningfully, but with a soft skills edge to facilitate sales, legal, and marketing conversations

• Ability to drive delivery across product, engineering, legal, sales, and marketing workstreams simultaneously

• Fluent English speaking for business communication is mandatory, Mandarin is a plus