Machine Learning Project Manager

We are seeking a highly skilled Machine Learning Project Manager to lead the end-to-end delivery of production-grade AI systems for enterprise clients in regulated industries. You will translate complex business and operational challenges into scalable, governed, and continuously improving ML solutions, ensuring technical excellence, regulatory compliance, and measurable impact.


Our work spans applied machine learning, large language model applications, and decision-intelligence platforms that operate against live enterprise data. Engagements vary by client and by domain, so you will move across problem spaces rather than owning a single fixed product.

Responsibilities

• Lead delivery of ML and AI systems from conception through deployment, live operation, and continuous improvement.
• Coordinate data scientists, engineers, client-side teams, and compliance stakeholders to execute technical roadmaps with precision.
• Own client engagement: requirement intake, working sessions, progress reporting, and demonstration of value to both practitioners and executives.
• Define success metrics tied to real business outcomes, and hold the delivery to them.
• Implement guardrails for ethical and regulatory compliance, including data segregation, access control, and full auditability of system outputs.
• Manage LLM integrations with strict safety controls: prompt validation, output filtering, hallucination detection, grounding of generated content in verifiable evidence, and usage monitoring.
• Ensure traceability from source data through features, model outputs, and downstream decisions, so any conclusion the system presents can be evidenced on request.
• Drive MLOps maturity: versioning, CI/CD pipelines, automated quality gates, monitoring, and retraining triggers.
• Communicate technical risks, limitations, and trade-offs clearly to technical and non-technical stakeholders, including executives.
• Document architecture, model behaviour, operational runbooks, and response protocols for knowledge transfer and compliance.
• Adapt quickly across domains and client environments as the project portfolio evolves.

Requirements

• Bachelor's or Master's in Computer Science, Engineering, Statistics, Data Science, or a related quantitative field.
• 3 to 5+ years managing production ML or data-intensive systems in enterprise environments.
• Proven experience leading cross-functional teams through the full ML lifecycle.
• Strong understanding of ML engineering practices, including how models behave once they are live rather than only in evaluation.
• Practical experience with LLM-based applications and their failure modes.
• Comfort working hands-on with messy real-world data and with stakeholders under time pressure.
• Demonstrated ability to navigate regulatory and ethical constraints in AI deployment, ideally in a regulated industry such as financial services, gaming, healthcare, or public sector.
• Excellent communication skills, able to align engineers, client teams, legal, and leadership around shared goals.
• Adaptability across problem domains, since engagements change as the portfolio grows.