Senior Technical Project Manager- Paris, France

Photon; Paris; Hybride; EUR 90 000 - 130 000; Plein temps

Résumé du poste

Photon is seeking a Senior Technical Project Manager to own end-to-end delivery of complex, multi-team programs spanning application engineering and data/AI/ML platforms. You will connect engineering, product, data science, architecture, QA, and business stakeholders to turn ambiguous goals into sequenced, resourced plans and drive them to production.

This hands-on role requires reading architectures, challenging estimates, and making credible trade-offs.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8–12 years of total experience with 4–5 years in technical software delivery programs.
• Hands-on experience delivering both application/product engineering and data/AI/ML initiatives.

Responsabilités

• Own end-to-end delivery for two or more concurrent technical programs.
• Build and maintain integrated delivery plans with milestones and entry/exit criteria.
• Identify and manage cross-team dependencies; drive resolution before slips.
• Run structured risk and issue management with mitigation options.
• Manage release readiness, go/no-go reviews, and post-launch stabilization.
• Partner with Engineering Managers and Product Owners for Agile squads.
• Review designs to ensure non-functional requirements are planned, not retrofitted.
• Track debt and its impact on delivery timelines.
• Coordinate data/AI/ML initiatives across data engineering/science/MLOps teams.

Connaissances

Leadership

Stakeholder management

Agile delivery

Risk management

Delivery planning

Executive communication

Formation

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

Outils

Jira

Confluence

Azure DevOps

MS Project

Description du poste

Senior Technical Project Manager

Software & Application Delivery | Data and AI/ML Programs

Job Title Senior Technical Project Manager

Department Technology / Program Delivery

Reports To Director of Program Management / Head of Delivery

Experience 8–12 years, including 4+ years managing technical programs

Location Onsite

Employment Type Full-time

ROLE SUMMARY

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 deliveryfor two or more concurrent technical programs, including scope, schedule, budget, dependencies, risks, and release readiness.
• Build and maintain integrated delivery planswith clear milestones, critical path, capacity assumptions, and explicit entry/exit criteria per phase.
• Identify and manage cross-team dependenciesacross squads, vendors, and platform teams; drive resolution before they become schedule slips.
• Run structured risk and issue managementwith mitigation owners and dates; elevate 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 Ownersfor Agile squads — backlog readiness, sprint planning, estimation, velocity, and definition of done.
• Review technical designs and solution approacheswith 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 debtand 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 acrossdata engineering, data science, and MLOps teams, ensuring handoffs between them are defined and instrumented.
• Ensure model performance, drift monitoring, and responsible-AI review gatesare 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 accountabilityfor 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 reportingreflecting 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 modelsas appropriate; improve delivery practices, templates, and metrics across the portfolio.
• Mentor junior project managers and scrum mastersand act as a force multiplier for the delivery function.
• Lead retrospectives and post-incident reviewsand 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.
• 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.

PREFERRED QUALIFICATIONS

• 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).

CORE COMPETENCIES

• 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.

SUCCESS IN THE FIRST 12 MONTHS

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.