ML Ops Engineer
London, United Kingdom
GBP 84K-110K (estimate) Senior-level Full Time
Tasks
- Add observability and telemetry
- Automate infrastructure using Infrastructure as Code
- Build MLOps pipelines for training and deployment
- Deploy Large Language Models
- Implement autoscaling and resource allocation
- Implement inference engines for low latency
- Manage Kubernetes deployments
- Monitor and optimize cloud spend
- Optimize GPU compute workloads
- Provision cloud AI ML infrastructure
- Set up model monitoring for drift and latency
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Ansible | Argo Workflows | ArgoCD | Bash | CI/CD | Cloud platform | Deep learning | DeepSpeed | Docker | GPU Operator | GitHub Actions | Go | Google Cloud | Google Cloud Platform | Grafana | Helm | Hugging Face | Hugging Face Transformers | Inference Server | Infrastructure as Code | Jenkins | Kernel tuning | Kubernetes | Langchain | Langsmith | Linux | Linux Kernel | Linux Kernel Tuning | MLflow | Microsoft Azure | NVIDIA GPU | NVIDIA GPU Operator | OpenTelemetry | Prometheus | Python | Ray | Slurm | TensorRT-LLM | Terraform | Triton Inference | Triton Inference Server | VLLM | Weights and Biases | “as-code”
Education
N/A
Roles
DevOps | DevOps Engineer | Engineer | ML Engineer | MLOps Engineer
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