Senior Staff MLOps Engineer (AWS)

TL;DR

Senior Staff MLOps Engineer (AWS/Python): Building and scaling a self-service ML platform for energy transaction infrastructure with an accent on experiment tracking, model registries, and production monitoring. Focus on designing heterogeneous workload platforms and implementing reliable backtesting and shadow deployment infrastructure.

Location: Remote (United Kingdom)

Compensation: £130K + Equity

Company

An AI-native transaction infrastructure company rebuilding the global energy market to make electricity trading transparent and fair.

What you will do

• Own the ML platform strategy and roadmap to move the infrastructure toward a fully self-service model.
• Lead the design and implementation of experiment tracking, model registries, and production monitoring across all model types.
• Build backtesting infrastructure and shadow deployment capabilities to validate forecasting and pricing models.
• Provide architectural vision and technical standards for the MLOps lifecycle as a force-multiplier for the team.
• Collaborate with ML and software engineers to translate commercial needs into principled platform decisions.
• Evaluate and select the MLOps tooling ecosyshirify.global to optimize for scale and specific workload mixes.

Requirements

• Must be based in the United Kingdom
• Experience scaling an ML platform from early-stage to best-in-class infrastructure in a fast-moving environment.
• Deep expertise in ML pipeline orchestration (Metaflow, Prefect, Airflow) and infrastructure (Sagemaker, Vertex AI, Chalk).
• Hands-on experience with experiment tracking (MLflow, W&B) and governance tooling for model fleets at scale.
• Strong proficiency in Python, AWS, and infrastructure-as-code (Terraform, AWS CDK).
• Proven technical leadership at Staff+ level, including defining platform direction and mentoring peers.

Nice to have

• Familiarity with Metaflow.
• Experience with operations research or large-scale optimization in production.
• Expertise in business-critical time series forecasting models.
• Exposure to reinforcement learning or production LLM workloads (e.g., fine-tuning).

Culture & Benefits

• Competitive salary and equity offers.
• Opportunity to work on critical infrastructure for global decarbonization.
• High-growth environment backed by a $75 million Series B.
• Culture of ownership and technical leadership in a scale-up setting.

Hiring process

• Initial 30-minute call with the Talent Team.
• 60-minute behavioral interview with the Head of Data.
• 90-minute technical interview with the team featuring a live technical exercise.
• 45-minute culture-add interview with cross-functional stakeholders.