AI Project Manager

THE ROLE

A rare dual threat — you manage the full lifecycle of AI projects with the rigour of a seasoned PM and the technical depth to challenge engineers, evaluate model decisions, and unblock every workstream. Both technical and non-technical teams will trust you equally.

This is not a role for someone who 'knows a bit about AI.' You will be expected to read model evaluation reports, challenge architecture decisions, define data labelling pipelines, and present a clear project status to the board — all in the same week. You are the person every team turns to when things get complex, and the one who makes complexity disappear.

WHAT MAKES THIS ROLE DIFFERENT

Deep technical fluency

• Understand ML model lifecycles end-to-end
• Review and challenge technical architecture
• Speak the language of data engineers and ML researchers

Full project ownership

• Own delivery from kickoff to production
• Manage budgets, timelines, and risks
• Keep every workstream accountable

Cross-functional leadership

• Bridge engineers and executives fluently
• Drive alignment across all stakeholders
• Own communication at every level

CORE RESPONSIBILITIES

Technical project management

• Own end-to-end delivery of AI/ML projects from scoping to production deployment
• Build project plans covering data, model, infra, and product tracks
• Facilitate technical design reviews, model eval sessions, and architecture decisions
• Manage MLOps dependencies — training runs, versioning, CI/CD, and rollout plans
• Define and track technical milestones: data readiness, model performance thresholds, latency SLAs
• Coordinate QA, testing, and shadow deployment phases before full launch

Delivery & execution

• Run agile ceremonies across hybrid technical and non-technical teams
• Manage scope, budget, resources, and timeline with rigour and transparency
• Proactively identify blockers and resolve them before they become delays
• Maintain RAID logs, project documentation, and post-mortems
• Coordinate data labelling, model training, and product integration workstreams
• Define and track project KPIs from model accuracy to business impact

Stakeholder management

• Serve as the primary point of contact for all project stakeholders
• Translate technical complexity into clear, actionable executive updates
• Manage expectations around AI uncertainty, iteration cycles, and timeline risks
• Build trust with engineering leads while keeping business stakeholders confident

AI governance & risk

• Ensure all AI projects follow responsible AI and data governance standards
• Identify and mitigate risks: model bias, data privacy, compliance, and technical debt
• Coordinate with legal, security, and compliance teams on AI-specific requirements
• Own the risk register and ensure mitigation plans are always current

WHAT YOU BRING

Must-haves

• 5+ years of project management, with 2–3 years delivering AI or ML projects
• Strong technical foundation — able to read code, review ML pipelines, and understand model evaluation
• Hands-on familiarity with the full AI/ML lifecycle: data ingestion, training, evaluation, deployment, monitoring
• Experience managing cross-functional teams across data science, engineering, product, and business
• Proficiency with agile methodologies, sprint planning, and backlog management
• Excellent communication — equally fluent with engineers and C-suite
• Comfort navigating ambiguity, iteration, and research-driven workflows
• PMP, PRINCE2, PMI-ACP, or equivalent (or actively pursuing)

Nice-to-haves

• Degree in Computer Science, Data Science, Engineering, or related technical field
• Direct experience with LLMs, RAG pipelines, fine-tuning, or generative AI products
• Familiarity with MLOps tools — MLflow, Weights & Biases, Vertex AI, SageMaker
• Ability to write basic Python/SQL scripts to verify pipeline outputs
• Experience shipping AI features in regulated industries (finance, healthcare, legal)
• Background in change management and AI adoption across non-technical teams
• Knowledge of responsible AI frameworks and EU AI Act or equivalent regulation

CORE SKILLS

• AI/ML lifecycle
• Technical project delivery
• Agile & Scrum
• MLOps awareness
• Risk management
• Stakeholder alignment
• LLMs & Generative AI
• Budget & resource planning
• Data pipeline coordination
• Executive communication
• AI governance
• RAID logs
• Python / SQL (basic)
• Cross-functional leadership

WHAT SUCCESS LOOKS LIKE AT 6 MONTHS

• At least one AI project delivered end-to-end — on time, on scope, with measurable business outcomes
• Clear technical and business reporting cadence established across all active AI initiatives
• Trusted by both the engineering team and executive leadership simultaneously
• Risk register, governance process, and documentation standards in place for all projects
• Visibly improved velocity and reduced ambiguity across AI delivery workstreams

Job Types: Full-time, Fixed term contract

Contract length: 12 months

Pay: $105.00 per hour

Experience:

• Project management: 5 years (required)
• Delivering AI or ML projects: 2 years (required)

Work Location: Remote