MLOps Engineer – Creative Generative AI Studio

Permanent | Hybrid | Competitive package + equity

$200k-$250k

I’m working with one of the most exciting creative AI studios in the market right now a team that’s combining cutting-edge machine learning research with world-class creative talent to build the next generation of generative media experiences.

Think text-to-image, video, and interactive AI experiences that push the limits of what’s possible and then imagine working alongside the researchers, engineers, and artists making it happen.

They’ve engaged us exclusively to find a talented MLOps Engineer to own the infrastructure, tooling, and workflows that take groundbreaking AI models from the lab to production.

This isn’t a “keep the lights on” MLOps role it’s about enabling creative experimentation at scale, building pipelines that let the team ship ambitious new ideas in days, not months.

What you’ll be doing

• Designing and maintaining scalable ML infrastructure for training, testing, and deploying generative AI models.
• Building automated, production-grade data pipelines for model training and evaluation.
• Creating CI/CD workflows that make ML deployment seamless and reproducible.
• Managing cloud environments (AWS, GCP, or Azure) and distributed GPU compute.
• Integrating experiment tracking, model registries, and monitoring tools like MLflow or Weights & Biases.
• Working closely with ML researchers and creative technologists to productionise cutting-edge models.
• Optimising inference pipelines for real-time creative applications.

What they’re looking for

• 3+ years in MLOps, ML engineering, or a related field.
• Proficiency in Python; bonus for Go or TypeScript.
• Experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Strong cloud experience (AWS/GCP/Azure), containerisation (Docker), and orchestration (Kubernetes).
• Familiarity with ML pipeline tools (Kubeflow, SageMaker, Vertex AI) and workflow schedulers (Airflow).
• A genuine interest in generative AI and its creative applications.

Nice to have

• Experience deploying generative models (diffusion, transformers, GANs).
• Open-source contributions to ML tooling.

Why this role stands out

• Direct impact your infrastructure will power creative products and experiences seen by millions.
• True cross-disciplinary collaboration with engineers, researchers, and world-class creatives.
• A culture that values rapid experimentation without sacrificing quality.
• Remote-first and flexible working with a competitive package + equity upside.

If you’re an MLOps Engineer who thrives in fast-moving, creatively charged environments and you want your work to directly shape how the world experiences generative AI, this is one to explore.