Senior Technical Project Manager- Paris, France

Senior Technical Project Manager

We are looking for a Senior Technical Project Manager to own the end-to-end delivery of complex, multi-team technical programs spanning application engineering and data/AI-ML platforms. You will be the connective tissue between engineering, product, data science, architecture, QA, and business stakeholders — turning ambiguous goals into sequenced, resourced, measurable plans, and then driving them to production.

This is a hands-on technical role, not a status-reporting one. You are expected to read an architecture diagram, challenge an estimate, understand why a model is failing validation, and make credible trade-off recommendations. Success is measured by predictable delivery, healthy engineering teams, and outcomes stakeholders can point to.

Program & Delivery Ownership

• Own end-to-end delivery for two or more concurrent technical programs, including scope, schedule, budget, dependencies, risks, and release readiness.
• Build and maintain integrated delivery plans with clear milestones, critical path, capacity assumptions, and explicit entry/exit criteria per phase.
• Identify and manage cross-team dependencies across squads, vendors, and platform teams; drive resolution before they become schedule slips.
• Run structured risk and issue management with mitigation owners and dates; escalate early with options rather than problems.
• Manage release and launch readiness — go/no-go reviews, cutover plans, rollback criteria, hypercare, and post-launch stabilisation.

Software & Application Engineering

• Partner with Engineering Managers and Product Owners for Agile squads — backlog readiness, sprint planning, estimation, velocity, and definition of done.
• Review technical designs and solution approaches with engineering and architecture; ensure non-functional requirements (performance, security, scalability, observability) are planned, not retrofitted.
• Drive engineering discipline — CI/CD adoption, environment readiness, test automation coverage, and code quality gates — to reduce cycle time and defect leakage.
• Track and challenge technical debt and represent delivery impact in prioritisation discussions.

Data and AI/ML Programs

• Manage delivery of data and AI/ML initiatives — data ingestion and pipeline builds, platform migrations, analytics products, and ML model development through deployment.
• Understand the ML lifecycle — problem framing, data acquisition, feature engineering, training, evaluation, deployment, monitoring, and retraining — and plan realistically for experimentation cycles, data readiness gaps, and non-deterministic outcomes.
• Coordinate across data engineering, data science, and MLOps teams, ensuring handoffs between them are defined and instrumented.
• Ensure model performance, drift monitoring, and responsible-AI review gates are addressed as first-class delivery requirements, alongside data governance, lineage, privacy, and compliance obligations.
• Translate technical outcomes into business metrics; support ROI and value-realisation tracking for data and AI investments in partnership with product and finance.

Stakeholder Management & Governance

• Serve as the single point of accountability for program communication — status, forecasts, and decisions — for executive, business, and technical audiences at the right altitude for each.
• Facilitate steering committees, program reviews, and architecture/change boards; drive decisions to closure with documented rationale.
• Build and maintain delivery dashboards and reporting reflecting real signal, not vanity metrics.
• Support resource forecasting, vendor engagement, and SOW/change-order discussions; and manage third-party or offshore delivery partners against SLAs and quality expectations.

Process & Team Leadership

• Coach teams on Agile, Scrum, Kanban, or hybrid models as appropriate; improve delivery practices, templates, and metrics across the portfolio.
• Mentor junior project managers and scrum masters and act as a force multiplier for the delivery function.
• Lead retrospectives and post-incident reviews and drive measurable corrective actions.

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field — or equivalent practical experience.
• 8–12 years of total professional experience, with at least 4–5 years managing technical software delivery programs.
• Demonstrated ownership of at least one complex, multi-team program delivered to production with measurable business impact.
• Hands-on experience delivering both application/product engineering work and data or AI/ML initiatives.
• Strong working knowledge of modern software delivery: Agile/Scrum, CI/CD, cloud platforms (AWS, Azure, or GCP), APIs, microservices, and test automation.
• Working familiarity with the data and ML stack — data pipelines and warehousing, model training and evaluation concepts, and MLOps practices such as model versioning and monitoring.
• Proficiency with delivery and collaboration tooling: Jira, Confluence, Azure DevOps, MS Project or Smartsheet, and dashboarding tools.
• Proven ability to manage schedules, budgets, capacity plans, and vendor relationships for programs of meaningful scale.
• Excellent written and verbal communication; able to brief executives and debate design details with engineers in the same day.
• Track record of navigating ambiguity, competing priorities, and organisational friction without losing momentum.

• PMP, PMI-ACP, Certified Scrum Master (CSM), SAFe, or equivalent certification.
• Prior hands-on experience as a software engineer, data engineer, or architect.
• Experience scaling delivery across distributed or offshore teams and multiple time zones.
• Exposure to regulated environments and compliance frameworks (SOC 2, HIPAA, GDPR, PCI-DSS).
• Experience with cloud or data platform migrations, or with productionising generative-AI/LLM applications.
• Familiarity with FinOps, cloud cost governance, or engineering productivity metrics (DORA).

• Technical depth sufficient to earn engineering credibility and ask the second and third question.
• Structured thinking — decomposes ambiguity into plans with owners and dates.
• Bias to action and follow-through; closes loops without being chased.
• Influence without authority across functions and seniority levels.
• Calm, transparent judgement under pressure — surfaces bad news early, with options.
• Outcome orientation over activity reporting.

First 30 days

• Understand the portfolio, teams, architecture, delivery process, and stakeholder map; establish a baseline on program health.

First 90 days

• Own assigned programs end to end with a credible integrated plan, an active risk register, and reporting stakeholders trust.

First 12 months

• Deliver committed releases predictably, measurably improve delivery cycle time and quality, and raise the maturity of delivery practice across the teams you touch.