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MLOps Lead
MLOps Lead (United Kingdom – Remote)
This position is listed on behalf of a partner company, who manages all applications and next steps. As an MLOps Lead, you will shape the strategy, architecture, and operational excellence of a cutting-edge machine learning infrastructure, supporting large-scale AI systems.
Leading a team of MLOps engineers, you will bridge the gap between research and production, ensuring seamless deployment, monitoring, and scaling of ML models in high-performance environments. This role combines technical leadership and hands-on architectural decision-making, offering the chance to build scalable infrastructure while collaborating with engineering, research, and product teams. Working fully remote in a global setting, you will establish best practices, drive innovation, and enable reliable, scalable AI solutions.
Key Accountabilities
- Team Leadership & Mentorship
- Lead, mentor, and develop a high-performing MLOps team, fostering a culture of collaboration, technical excellence, and continuous improvement.
- Strategy & Roadmap
- Define and execute the MLOps roadmap, aligning infrastructure initiatives with research, engineering, and product goals.
- Infrastructure Design & Development
- Design, implement, and maintain scalable machine learning infrastructure, including:
- Automated training pipelines
- CI/CD workflows
- Orchestration frameworks
- Deployment processes
- Drive architectural decisions for model-serving platforms, prioritising low-latency, high-throughput inference via modern serving technologies.
- Design, implement, and maintain scalable machine learning infrastructure, including:
- Feature Stores & Data Pipelines
- Build and optimise feature stores, data pipelines, and storage solutions for large-scale model training and production inference.
- Research-Production Transition
- Collaborate closely with research teams to streamline model handoff from experimentation to production.
- Monitoring & Observability
- Establish monitoring, logging, alerting, and observability strategies for:
- Ensuring model performance
- Highlighting system reliability
- Early detection of drift or operational issues
- Establish monitoring, logging, alerting, and observability strategies for:
- Operational Excellence
- Define engineering standards, operational best practices, and scalable infrastructure processes for long-term growth.
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Requirements
-
Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical field (or equivalent hands-on experience).
- Minimum 7 years of expertise in MLOps or ML infrastructure engineering, including 3 years in a leadership role.
-
Technical Specialisations
- Proficiency in Python; complementary knowledge of Bash/Go.
- Depth in building end-to-end MLOps infrastructure from scratch.
- In-depth familiarity with:
- Machine learning platforms/fFrameworks: MLflow, Weights & Biases (W&B), PyTorch, TensorFlow.
- Model serving tools: Triton, TorchServe, TF Serving, KServe.
- Kubernetes and cloud platforms (AWS/GCP/Azure).
- Infrastructure as code: Terraform, Helm, GitOps.
- Production-grade data pipelines and monitoring (Prometheus, Grafana, OpenTelemetry).
- Plus-point experience with:
- Workflow orchestration, FastAPI, Databricks, Snowflake, LLMs, SRE practices, or AI startups.
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- Soft Skills
- Effective communication across multidisciplinary teams (research, engineering, product).
- Strong collaborative mindset for mission-critical, high-impact initiatives.
Benefits
- Competitive Compensation
- Salary + equity participation.
- Healthcare
- Comprehensive coverage for employees and dependents.
- Family Support
- Generous paid parental leave (biological, adoptive, surrogate).
- Relocation Assistance
- Support for office-based transitions.
- Flexible Work
- Fully remote with international collaboration.
- Ownership & Growth
- Opportunity to lead cutting-edge AI infrastructure with full technical ownership.
- Culture
- Inclusive, mission-driven environment that values:
- Innovation
- Collaboration
- Diversity of thought
- Continuous learning
- Inclusive, mission-driven environment that values:
Applications managed by partner company via Jobgether. This initiative leverages AI-driven matching for objective, fair evaluation. The hiring decisions and next steps (interviews, etc.) remain fully under the employer’s control.
Note: By applying, you confirm consent to data processing under GDPR, including access, rectification, or objection rights at any time.#LI- CL1
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