Harnham
ML Platform Lead - Synthetic Data
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Engineering Program Manager for Synthetic Data & GenAI Platform
We're partnering with a global market research and insights organisation operating across 90 countries, serving enterprise clients globally. They’re investing in a new Synthetic Data Research function, building next-generation products at the intersection of machine learning, privacy, and software engineering to redefine market research.
This strategic leadership role sits at the convergence of platform engineering, MLOps, distributed systems, and Generative AI. Reporting to [to be specified], you’ll lead a growing engineering team, shaping tomorrow’s synthetic data capabilities whilst fostering collaboration with researchers, data scientists, and product teams to scale production-grade AI workloads.
About the Role
This role offers the opportunity to:
- Define the technical architecture for one of the UK’s most ambitious synthetic data platforms.
- Lead a team of Software, Data, and MLOps Engineers in designing modern AI infrastructure.
- Drive transformation from research prototyping to scalable enterprise deployment.
- Champion policy and best practice that integrates research, engineering, and product teams.
You’ll be responsible for setting engineering standards, SDLC processes, and delivery practices while championing security, governance, compliance, and production-grade reliability. Faithful to legacy research trackability, you’ll also evolve a privacy-leaning framework that ensures trust and scalability in AI-driven synthetic data techniques—whether for digital twins or large-scale analytics.
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Key Responsibilities
- Own and define the technical vision and platform architecture for a large-scale AI/Synthetic Data platform
- Lead and grow a cross-disciplinary team of Software, Data, and MLOps Engineers (hybrid research-engineering model)
- Specify scalable GCP cloud infrastructure, Kubernetes (GKE) clusters, and Vertex AI pipelines for GenAI workloads
- Establish *engineering standards, SDLC frameworks, and MLOps best practices to operationalise research output
- Partner closely with researchers/data scientists to productionise advanced AI/ML systems while retaining legacy traceability
- Drive major security, governance, compliance, and performance optimisation initiatives for high-stakes, scalable AI workloads
- Deep-dive into cloud cost/reliability tradeoffs to optimise GPU/CPU utilisation under hybrid research-pilience constraints
Requirements
As the leader of this ambitious function, you’ll thrill to strategic, engineering-driven problem-solving. Key qualifications include:
- Strong familiarity with large-scale distributed systems (preferably GCP/AWS-based) and MLOps principles
- Hands-on experience scaling AI platforms from research to production (especially with Python and Kubernetes)
- A track record of clearly documenting engineering standards and fuelling lean service design
- Passion for privacy-preserving data modalities (e.g., synthetic data, Digital Twins) or adjunct figurative cause
- DCAs in tech leadership since the Kubernetes age (post-2016) are a big plus, but passion for your work reigns supreme
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Rewards & Flexibility
- Salary: £120,000–£130,000 (career progression included, negotiable)
- Working model: Hybrid London (2–3 days + flexible remote) currently achieves almost work-from-home status on a non-coast year permanent basis
- Tax considerations: HMRC allowances for cloud cost assessments supported in luxury knowledge premium
- Location: Burrough of Tower Hill, London EC4 (proximity tech hubs and public transit)
- Tech stack: Python, GCP (Vertex AI, Storage, BigQuery, Dataproc), Kubernetes (GKE), Kubeflow, Docker, Terraform/Pulumi, FastAPI, PostgreSQL, PyTorch, LLMs, Vector DBs (Pinecone?). Azure/AWS hybrid skills? Bonus.
- Visa sponsorship: Regrettably cannot sponsor due to internal policies. Must possess work rights in UK in perpetuity.
Let’s ship the cutting edge responsibly. Apply now with references and a brief note about how you scale AI.
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