MLOps Engineer

We are working in partnership with a global, technology-driven insights organisation to hire an AI & MLOps Engineer to join a cutting-edge Synthetic Data team. This is a high-impact opportunity to work at the forefront of generative AI and machine learning platforms, helping turn advanced research into scalable, production-grade systems used across a global business.

The Opportunity

You'll join a multidisciplinary team building a next-generation platform focused on:

• Synthetic data generation at scale
• AI-powered data augmentation tools
• "Digital twin" models powered by LLMs
• Privacy-first, enterprise-grade ML infrastructure

The team blends data science, software engineering, and research, with strong links to leading academic institutions - ensuring the work is both scientifically rigorous and commercially impactful.

The Role

As an AI & MLOps Engineer, you'll play a critical role in bridging research and production, ensuring complex models are deployed in a reliable, scalable and cost-efficient way. Key responsibilities include:

• Productionising cutting-edge AI models (LLMs, diffusion models, synthetic data generators)
• Designing and maintaining scalable ML pipelines and workflows
• Building fault-tolerant orchestration layers for long-running, compute-heavy jobs
• Implementing CI/CD pipelines for machine learning, including model testing and versioning
• Driving observability and monitoring, including model performance, data drift, and system health
• Optimising cloud infrastructure and compute usage (GPU/CPU, caching, scaling strategies)
• Developing robust data architectures and asynchronous processing systems

You'll work closely with applied ML researchers, acting as the key link that ensures innovation is translated into real-world, production-ready solutions.

Technology Environment

You'll be working across a modern AI/ML stack including:

• Languages & Frameworks: Python, PyTorch
• MLOps & Platforms: Kubeflow, Vertex AI, Kubernetes, Docker
• Backend Systems: FastAPI, async job queues (Celery/RabbitMQ)
• Data & Storage: GCP, BigQuery, Parquet/Arrow, vector databases
• LLM Tooling: RAG architectures, PEFT/LoRA fine-tuning

What We're Looking For

MLOps & Engineering Expertise

• Strong experience building and managing complex ML pipelines (DAGs)
• Proven ability to deploy generative AI models into production
• Hands-on with CI/CD for ML, model registries, and reproducibility

Data & Systems Engineering

• Experience designing high-throughput data pipelines across structured and unstructured data
• Strong understanding of asynchronous systems and APIs
• Expertise in data validation and schema enforcement

AI/ML Knowledge

• Solid Python and PyTorch skills
• Familiarity with LLMs, diffusion models, or similar architectures
• Experience with model monitoring, evaluation, and performance optimisation

Why Apply?

• Work on cutting-edge generative AI use cases with real-world impact
• Be part of a high-performing, research-driven engineering team
• Shape how advanced ML systems are deployed at global scale
• Gain exposure to complex challenges across AI, data, and platform engineering

If you're passionate about building robust ML systems and want to work at the bleeding edge of AI innovation, we'd love to hear from you.