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MLOps Engineer

Icresources — United Kingdom · Posted ~1 hour ago

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Skills

MLOps Production ML pipelines ML infrastructure Experiment tracking Model management Model deployment Python Docker Kubernetes Slurm Distributed systems CI/CD Performance optimization PyTorch JAX Distributed Systems

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Summary ✨ AI‑Generated

An MLOps engineer is sought to build and maintain production machine-learning pipelines and infrastructure for a next-generation computing platform. Responsibilities include experiment tracking, model management and deployment, container orchestration, distributed systems, CI/CD, and performance profiling and benchmarking. Strong Python skills and familiarity with modern ML frameworks are expected, with an emphasis on reliable, efficient large-scale AI deployment.

Highlights

Opportunity to help develop a next-generation computing platform aimed at improving AI performance, efficiency, and deployment economics. The role combines MLOps, ML infrastructure, distributed systems, deployment automation, and AI performance optimization in a deep-tech environment.

Description

I’m working with an innovative deep-tech company developing next-generation computing technology designed to overcome some of the biggest performance and efficiency challenges facing modern AI. They’re building a new class of AI acceleration platform that enables complex workloads to run faster, more efficiently, and at significantly lower cost than traditional approaches. This is an opportunity for an MLOps Engineer to join a highly talented team to develop a breakthrough computing platform with the potential to transform large-scale AI deployment. What They’re Looking For MLOps experience- building and maintaining production ML pipelinesML infrastructure expertise- experiment tracking, model management, and deploymentStrong Python skills- experience with PyTorch, JAX, or similar frameworksPlatform engineering- Docker, Kubernetes, Slurm, and distributed systemsCI/CD knowledge- automating testing, validation, and deploymentsPerformance optimisation- profiling and benchmarking AI workloadsInfrastructure as Code- Terraform, Ansible, or similar toolsMonitoring & observability- logging, metrics, and alerting systemsCollaborative mindset- working across ML, hardware, and compiler teamsDeep-tech experience- ideally within AI, semiconductors, or hardware startups Why Consider It Own MLOps for a pioneering AI hardware companyWork on breakthrough technology at the forefront of AI computeSolve complex technical challenges on novel accelerator hardwareInfluence product direction with high visibility and ownershipCompetitive package including salary, equity, and benefits Please contact Chris Amison for more information.