Senior Staff MLOps Engineer

Who We Are: We are rebuilding the energy transaction, making it transparent and fair. Our goal is to put power back where it belongs, in the hands of customers and to take on one of the most critical problems of our century, access to low cost electricity. tem exists to fix a broken global energy market that's long favoured legacy operators, intermediaries, and opaque pricing. Today's electricity system was not designed for rapid decarbonisation, AI-driven efficiency or fair access for the actual users - businesses and generators. We've built the first AI native transaction infrastructure to reinvent how electricity is bought, sold and priced. Our technology is designed to cut out the inefficient fees, automate complex market flows, and bring transparency and fairness to energy transactions at scale.

The Role: Rosso is tem's core IP, the transaction infrastructure that prices electricity for thousands of businesses, balances portfolios in real time, and sits on the critical path for every deal tem closes. The machine learning models inside Rosso forecasting, pricing, and optimisation are what make those decisions possible. Every inference shapes the prices our customers see. Today, tem's ML platform has solid foundations: Metaflow for orchestration, AWS Batch for compute, and automated CI/CD pipelines already in place. That's got Rosso to where it is. But as the number of model types grows and Rosso scales, the platform needs the next layer: structured experiment tracking, a model registry, production monitoring, and self-service tooling that lets ML engineers move at pace without being blocked on infrastructure. This role exists to build that layer and define what the platform looks like at scale.

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. Keep the platform aligned to Rosso's commercial trajectory. 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 against historical data and in production before they go live. Set technical direction: Provide the architectural vision and standards the Senior MLOps Engineer executes against. This is a force-multiplier relationship, not a management one. Partner across the team: Work closely with ML engineers and software engineers to understand what the platform needs to unlock the next wave of Rosso capabilities. Translate those needs into principled platform decisions. Choose the right tools: Evaluate the MLOps tooling ecosystem with clear eyes. Make choices that fit tem's scale and workload mix not what's fashionable. Drive deployment reliability: Push toward more frequent, reliable model deployment cycles as Rosso moves from batch-heavy workflows toward live, near-real-time processes. Define best practices: Establish standards for how models are trained, versioned, deployed, and monitored across the team. Create a platform ML engineers trust.

Requirements: Must-Haves: Scaled an ML platform from early-stage: Demonstrable experience taking an ML platform from early stages to best-in-class infrastructure at a fast-moving company. You've been there, done it, and you're comfortable with the messiness and ambiguity that comes with scale-up life. ML pipeline expertise: Deep experience across the whole MLOps lifecycle 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 for model fleets at scale. Knows what good looks like and what to avoid. Broad MLOps tooling knowledge: Across the ecosystem monitoring, drift detection, CI/CD for ML, containerisation, IaC (Terraform, AWS CDK). Able to evaluate trade-offs and make principled choices for a specific context, not just default to what they know. Technical leadership track record: Evidence of setting platform direction, influencing cross-functional teams, and defining standards at Staff+ level. Raises the quality bar through design reviews, code reviews, and mentoring. Knows when to drive strategy and when to get into the weeds. Heterogeneous workload experience: Experience designing and operating platforms serving heterogeneous workloads (e.g. forecasting, classification, operations research, etc), not just one model type across batch and real time applications. Python, AWS + IaC: Strong Python; hands-on experience with AWS and infrastructure-as-code (Terraform, AWS CDK).

Benefits: Offers Equity