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MLOps Lead
MLOps Lead (United Kingdom, Fully Remote)
Our partner company is seeking an MLOps Lead to design and oversee a scalable, cutting-edge machine learning (ML) infrastructure for large-scale AI systems.
As the MLOps Lead, you will bridge the gap between research and production, ensuring seamless deployment, monitoring, and scaling of ML models. You will lead a team of MLOps engineers, driving technical excellence, collaboration, and process improvements across the entire ML lifecycle in a fully remote, international environment.
This role combines technical leadership, architectural decision-making, and hands-on execution, with the opportunity to build and refine ML infrastructure from the ground up while collaborating closely with engineering, research, and product teams.
Key Accountabilities
- Lead, mentor, and develop a high-performing team of MLOps engineers, fostering a culture of collaboration, technical excellence, and continuous improvement.
- Define and execute the MLOps roadmap, aligning infrastructure initiatives with research, engineering, and product objectives.
- Design, implement, and maintain scalable ML infrastructure, including:
- Automated training pipelines
- CI/CD workflows
- Orchestration frameworks
- Deployment processes
- Drive architectural decisions for model serving platforms, ensuring:
- Low-latency, high-throughput inference
- Integration with modern serving technologies (Triton Inference Server, TorchServe, KServe, etc.)
- Build and optimize feature stores, data pipelines, and storage solutions for:
- Large-scale model training
- Production-grade inference
- Collaborate closely with research teams to streamline the transition from experimentation to production environments.
- Establish monitoring, logging, alerting, and observability strategies to ensure:
- Model performance tracking
- System reliability
- Early detection of drift or operational issues
- Define engineering standards, operational best practices, and scalable infrastructure processes for long-term platform growth.
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Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
- Minimum of 7 years of MLOps or ML infrastructure engineering experience, including:
- At least 3 years in a technical leadership role.
- Strong software engineering expertise in:
- Python (primary), with working knowledge of Bash and/or Go.
- Proven experience in:
- Building, scaling, and leading MLOps infrastructure from the ground up.
- Deep knowledge of:
- ML platforms & frameworks (MLflow, Weights & Biases (W&B), PyTorch, TensorFlow).
- Model serving technologies (Triton Inference Server, TorchServe, TensorFlow Serving, KServe).
- Hands-on expertise with:
- Kubernetes
- Cloud platforms (AWS, GCP, Azure)
- Infrastructure as Code (Terraform, Helm, GitOps)
- Production-grade data pipelines
- Strong experience with:
- Monitoring & observability tools (Prometheus, Grafana, Datadog, OpenTelemetry).
- Excellent communication skills for cross-team collaboration (research, engineering, product).
- Advantageous but not required:
- Experience with workflow orchestration (FastAPI), Databricks, Snowflake, LLM infrastructure, SRE practices, or AI startups.
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Benefits
- Competitive compensation package (salary + equity participation).
- Comprehensive healthcare coverage (employees + eligible dependents).
- Generous paid parental leave (supports biological, adoptive, and surrogate parenthood).
- Relocation assistance (if applicable).
- Fully remote work environment with international collaboration opportunities.
- Technical ownership of cutting-edge AI infrastructure initiatives.
- Inclusive, mission-driven culture that values:
- Innovation
- Collaboration
- Diversity of thought
- Continuous learning
About the Hiring Process
Applications are managed through Jobgether, an AI-driven matching platform that:
- Evaluates candidates quickly, objectively, and fairly against role requirements.
- Shares shortlists directly with the hiring company.
- Ensures final decisions are made by the partner team (no automated hires).
Data Privacy Note: By applying, you consent to:
- Jobgether processing your data to evaluate candidacy (legitimate interest/pre-contractual basis).
- Sharing details with the hiring employer under GDPR (standard employment rules apply).
- Occasional AI-assisted screening, but human final review remains mandatory.
Apply today to join a team shaping the future of MLOps and AI infrastructure!
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