Senior Staff MLOps Engineer

Location & Employment

• Location: United Kingdom (Remote)
• Employment Type: Full time
• Department: Data
• Compensation: £130K • Offers Equity

Role Overview

Rosso is [Employer hidden — sign up to reveal]'s core IP, the transaction infrastructure that prices electricity for thousands of businesses. The machine learning models inside Rosso forecasting, pricing, and optimisation are what make those decisions possible. This role exists to build the next layer of the ML platform: structured experiment tracking, a model registry, production monitoring, and self-service tooling. You will join the Rosso service alongside a Senior MLOps Engineer in a cross-functional team of ML engineers and software engineers.

Responsibilities

• Own the ML platform strategy: Define the roadmap from Level 1 to Level 2, making architectural decisions ahead of when they'd otherwise become blockers.
• Build the foundations: Lead the design and build of experiment tracking, model registry, automated pipeline infrastructure, and production monitoring across all model types.
• Deliver backtesting and shadow deployments: Build the infrastructure the forecasting and pricing teams need to validate models reliably.
• Set technical direction: Provide the architectural vision and standards the Senior MLOps Engineer executes against.
• Partner across the team: Work closely with ML engineers and software engineers to understand what the platform needs.
• Choose the right tools: Evaluate the MLOps tooling ecosystem with clear eyes.
• Drive deployment reliability: Push toward more frequent, reliable model deployment cycles.
• Define best practices: Establish standards for how models are trained, versioned, deployed, and monitored.

Requirements

Must-Haves

• Scaled an ML platform from early-stage: Demonstrable experience taking an ML platform from early stages to best-in-class infrastructure.
• ML pipeline expertise: Deep experience with ML pipeline orchestration (Metaflow, Prefect, Airflow or equivalent) and ML infrastructure (Sagemaker, Vertex AI, Chalk, or equivalent).
• Model lifecycle tooling: Hands-on experience building or operating experiment tracking systems (MLflow, W&B, or similar), model registries, and governance tooling.
• Broad MLOps tooling knowledge: Across monitoring, drift detection, CI/CD for ML, containerisation, IaC (Terraform, AWS CDK).
• Technical leadership track record: Evidence of setting platform direction, influencing cross-functional teams, and defining standards at Staff+ level.
• Heterogeneous workload experience: Experience designing and operating platforms serving heterogeneous workloads.
• Python, AWS + IaC: Strong Python; hands-on experience with AWS and infrastructure-as-code (Terraform, AWS CDK).

Bonus Points

• Worked in a role where ML is at the core of the product
• Familiarity with Metaflow specifically
• Experience with operations research, large-scale optimisation
• Experience with business critical time series forecasting models
• Exposure to reinforcement learning or production LLM workloads

Interview Process

• First call with our Talent Team (30 mins)
• Behaviour Interview with Tim, Head of Data (60 mins)
• Technical Interview with the Team (90 mins)
• Culture-Add Interview with Stakeholders (45 mins)

We welcome applications from people of all backgrounds.

Compensation Range: £130K