About The Role

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 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.

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.