Senior Technical Project Manager

Photon; Paris; Sur place; EUR 110 000 - 150 000; Plein temps

Résumé du poste

Photon in Paris is seeking a Senior Technical Project Manager to own 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.

This hands-on role requires reading architecture diagrams, challenging estimates, and making credible trade-off recommendations.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8–12 years delivering technical software programs.
• Proven ownership of multi-team programs with measurable impact.

Responsabilités

• Own end-to-end delivery for two or more concurrent technical programs.
• Collaborate with engineering, product, data science, architecture, QA and business stakeholders.
• Review designs and ensure non-functional requirements are planned.
• Drive release readiness, risk management and governance.

Connaissances

Agile Methodologies

CI/CD

Cloud Platforms (AWS/Azure/GCP)

APIs & Microservices

Test Automation

Formation

Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field

Outils

Jira

Confluence

Azure DevOps

MS Project

Smartsheet

Description du poste

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.

KEY RESPONSIBILITIES

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; escalation 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.

REQUIRED QUALIFICATIONS

• 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.