Head of Platform & Engineering, Synthetic Data in London

The Synthetic Data Research team is building Ipsos’ next-generation platform for synthetic data and generative AI, turning cutting‑edge methods into practical tools that can be used safely and confidently across the business. We focus on two core products:

• Data Augmentation Workbench: a self‑serve internal platform that enables teams to train models and generate synthetic data through secure APIs and streamlined workflows, with evaluation and governance built in from day one.
• Digital Twins: agentic, respondent‑grounded LLM “synthetic panellists” designed to simulate behaviours and survey responses, supported by rigorous validation, privacy safeguards, and strong auditability.

Our work sits at the intersection of software engineering, machine learning, privacy, and market research methodology. We collaborate with leading academic institutions to ensure our approach is scientifically robust while remaining focused on real‑world impact. Ultimately, our goal is to deliver data collection efficiencies, new product innovation, and defensible scientific frameworks that can scale to thousands of colleagues and clients.

You’ll lead the engineering function of the Synthetic Data Team, setting technical direction, building a high‑performing team, and delivering a secure, reliable, compliant platform used by internal stakeholders globally. You’ll raise the bar on engineering excellence in the whole of Ipsos, setting the standard across the business, while partnering closely with data science, research methodologists, product, security/privacy, and legal.

In practice, you will:

• Build a platform people trust: Establish reliability, security, governance, and auditability so synthetic outputs can be used with confidence in business‑critical contexts.
• Standardise how ML work ships: Create repeatable patterns for data → training → evaluation → release using Vertex AI Custom Jobs, Experiments, and Pipelines, so we can move fast without losing reproducibility.
• Enable self‑serve at scale: Make it easy for internal teams to generate synthetic data and run experiments through stable APIs, clear documentation, and strong operational support.
• Operationalise privacy‑by‑design: Embed controls and guardrails that reflect respondent‑data sensitivity and MR methodology requirements.
• Create a healthy delivery machine: Mature the SDLC, sprint execution, and engineering standards across a mixed team of backend, data, MLOps, and infra.

Infrastructure & Cloud: Google Cloud Platform (GCP), Kubernetes (GKE), Docker, Infrastructure as Code (Terraform/Pulumi).

MLOps & Pipeline Platforms: Kubeflow Pipelines (KFP), Vertex AI, Model Registries, and automated ML CI/CD systems.

Backend & Data Services: Python, FastAPI, Asynchronous task brokers (Celery/RabbitMQ), PostgreSQL, Vector Databases.

Data Architecture: High‑throughput columnar stores (BigQuery, Parquet) and distributed processing frameworks.

AI/ML Foundations: Ecosystems supporting PyTorch, Large Language Models (LLMs), and Deep Generative Tabular Models.

What you’ll do:

• Define Technical Vision & Strategy: Own the high‑level architecture and platform strategy. You will design the roadmap for scaling our infrastructure to support heavy, asynchronous GenAI workloads.
• Lead & Scale the Team: Build, mentor, and manage a high‑performing team of Software Engineers, Data Engineers, and MLOps Engineers.
• Bridge Research & Engineering: Act as the critical translator between our Applied ML Scientists and the Engineering team.
• Ensure Enterprise Readiness: Drive initiatives around system security, data privacy, compliance, and high availability.
• Optimize Cloud Economics: Take ownership of cloud infrastructure costs.
• Platform Evangelism: Standardize our internal developer platform.

What you’ll need:

• Technical Leadership Engineering Management: Proven track record of managing, scaling, and mentoring cross‑functional engineering teams.
• Strategic Architecture: Deep experience designing highly available, distributed, and asynchronous systems at scale.
• Cloud & Infrastructure: Strong background in cloud‑native ecosystems (GCP preferred) and container orchestration (Kubernetes).
• GenAI & MLOps Domain Expertise: Experience leading teams that build or manage machine learning infrastructure.
• Business & Operational Acumen: Exceptional communication skills with a proven ability to align technical roadmaps with business objectives.

We offer a comprehensive benefits package designed to support you as an individual. Our standard benefits include 25 days annual leave, pension contribution, income protection and life assurance. In addition, there are a range of health & wellbeing, financial benefits and professional development opportunities. We have a hybrid approach to work and ask people to be in the office or with clients for 3 days per week. We appreciate you may have commitments outside of work. We are committed to equality, treating people fairly, promoting a positive and inclusive working environment and ensuring we have diversity of people and views.