MLOps Engineer

AI & MLOps Engineer

UK Remote | Generative AI | Cloud-Native ML Systems

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.

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
.

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:

• Product ionising 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
.

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

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

• 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

• 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

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

• 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.