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 tackle one of the most critical problems of our century: access to low‑cost electricity. tem exists to fix a broken global energy market that has long favored 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 eliminates inefficient fees, automates complex market flows, and brings 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—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. 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.
• 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.
• 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.
• Choose the right tools: Evaluate the MLOps tooling ecosystem with clear eyes.
• 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.

What Success Looks Like

MLOps is no longer a bottleneck; ML engineers are unblocked to focus on model quality. The time to deploy new machine‑learning models goes from days to minutes. The core features required from the machine‑learning platform are delivered before they block progress—e.g., backtesting and experiment tracking.

Requirements

• 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.
• 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.
• Broad MLOps tooling knowledge: Across the ecosystem 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 (e.g., forecasting, classification, operations research).
• Python, AWS + IaC: Strong Python; hands‑on experience with AWS and infrastructure‑as‑code (Terraform, AWS CDK).

• Worked in a role where ML is at the core of the product.
• Familiarity with Metaflow specifically.
• Experience with operations research, large‑scale optimisation in a production context.
• Experience working with business‑critical time‑series forecasting models.
• Exposure to reinforcement learning in a production setting.
• Exposure to production LLM workloads, e.g., fine‑tuning.

We welcome applications from people of all backgrounds, experiences, and identities, including those that are traditionally underrepresented in the tech and energy sectors. If you're excited about this role but not sure you meet every requirement, we'd still love to hear from you. Your unique perspective could be exactly what we're looking for.