Senior Technical Project Manager – AI Engineering

Role Overview

Join as a Senior Technical Project Manager specializing in AI Engineering within the AI Engineering and Product Delivery department. This role demands direct involvement in the full lifecycle of AI projects—from planning and scoping through execution, assessment, and culminating in production releases.

Primary Responsibilities

• Lead comprehensive project management activities, including formulating execution plans, tracking milestones, handling dependencies, and guaranteeing consistent delivery.
• Translate organizational goals into definitive project scopes, timelines, progress benchmarks, and measurable success indicators.
• Assign and oversee task ownership, set acceptance criteria, estimate efforts, and monitor daily progress aligned with established plans.
• Manage resource allocation, coordinate inter-product dependencies, and resolve conflicts or competing priorities across engineering units.
• Identify risks and impediments early, drive swift resolutions, and escalate critical issues with actionable proposals to leadership.
• Govern changes in project scope, schedule, or resources in collaboration with decision-makers, maintaining updated baselines.
• Collaborate with engineering leads to assess the suitability and technical feasibility of AI/ML models against business challenges.
• Challenge inflated engineering estimates and complex designs; promote the adoption of straightforward heuristics or conventional software solutions when preferable to AI implementations.
• Conduct trade-off analyses considering model accuracy, response time, operational costs, maintenance, and system reliability.
• Promote reuse of AI components and architectural patterns across products to optimize efficiency.
• Ensure development of thorough evaluation frameworks including benchmarks and statistical quality requirements at each project milestone.
• Differentiate between prototype demonstrations and genuine production readiness; rigorously validate claims concerning model performance and completion status.
• Enforce production standards encompassing real-time system monitoring, failure mitigation, data security protocols, and integration of human oversight where necessary.
• Provide evidence-based reporting using tangible proofs such as demos, test results, evaluations, and deployment logs.
• Maintain transparent and comprehensive status reporting for executive leadership including risks, unresolved decisions, and future steps.
• Monitor for scope creep, redundant work, or technical endeavors lacking clear business benefit, explicitly communicating impacts of changes.
• Document critical technical and operational decisions ensuring consistent alignment with delivery objectives.
• Implement and standardize delivery processes with clear Definitions of Ready (DoR), Definitions of Done (DoD), and release criteria.
• Conduct focused planning meetings, risk assessments, and milestone demonstrations to improve delivery predictability while minimizing administrative burden.

Qualifications and Experience

• Extensive experience as a Technical Project Manager, Technical Program Manager or Engineering Lead handling complex software deliveries.
• Proven hands-on expertise with AI/ML system development, testing, or deployment in live production settings—beyond basic usage of AI APIs or large language model prompting.
• Track record of managing intricate project plans, balancing cross-team trade-offs, and ensuring timely product releases.
• Strong technical knowledge of AI development processes, performance evaluation metrics, trade-offs of latency and cost, and production monitoring needs.
• Excellent communication skills capable of engaging both AI engineers on technical details and C-suite executives on risk and progress matters.
• Analytical thinker with a data-driven approach to verifying progress and resolving issues.

Ideal Candidate Profile

• Suited for a seasoned technical leader who integrates stringent project management with practical AI engineering insights, demanding verifiable progress evidence and holding teams accountable for production standards.
• Unsuitable for non-technical project managers focused only on task tracking or AI researchers disinterested in delivery and governance aspects.