Staff MLOps Engineer

Skills

About the Role

You will define and build Elliptic's Enterprise MLOps platform, creating the unified layer that ties together training, deployment, monitoring, and governance across the organization. You'll serve four distinct internal consumer groups with different needs, from reproducible training pipelines and CI/CD for customer-facing models, to rapid experimentation and GPU orchestration for research teams, to closing audit and compliance gaps for InfoSec, to reliable batch inference for Operations. You'll design a platform that enforces governance rigorous enough for a regulated financial crime context while staying flexible enough for fast-moving research teams. You will make build-vs-buy decisions, work hands-on with a small group of infrastructure engineers to ship production-grade capabilities, and onboard data scientists and ML engineers onto the platform through documentation, runbooks, and reference architectures.

Requirements

• Have built MLOps platforms or ML infrastructure from the ground up
• Have operated in a regulated industry and have hands-on experience building ML infrastructure to meet regulatory demands
• Comfortable operating in ambiguity and making decisions with incomplete information
• Able to influence through clarity, evidence, and quality of work rather than positional authority
• Write production-grade, tested, observable, and documented code
• Deep hands-on experience building MLOps platforms, including model registries, feature stores, and ML pipeline orchestration
• Working knowledge of model serving patterns: real-time inference, batch prediction, A/B deployment, and deployment strategies
• AWS infrastructure experience (ECS/EKS, S3, IAM, networking) and comfort operating in a Databricks ecosystem or equivalent lakehouse architecture
• Experience with model monitoring: model evaluation, data drift detection, prediction drift, and performance degradation alerting
• Track record of building something from zero and bringing it to a state where others could operate and extend it
• Experience in a regulated industry (fintech, financial services, healthcare) where model governance is a compliance requirement
• Prior experience running formal build-vs-buy evaluations with written decision records

Responsibilities

• Define the target-state MLOps architecture covering model training pipelines, serving infrastructure, monitoring, feature management, and governance
• Produce architecture decision records that inform investment decisions
• Make and document build-vs-buy-vs-stop recommendations with cost modelling and trade-off analysis
• Evaluate vendors, open-source tools, and managed services against company constraints
• Work with InfoSec to improve the model registry and model risk management framework
• Close gaps in metadata, lineage, approval workflows, and drift/bias detection
• Build model training pipelines, CI/CD for ML, and serving infrastructure
• Work directly with a small group of infrastructure engineers to ship production-grade platform capabilities
• Instrument observability across the ML lifecycle including training metrics, serving latency and throughput, data quality, and prediction drift
• Integrate observability with the existing observability stack
• Work with data scientists and ML engineers across all consumer groups to onboard them onto the platform
• Write documentation, runbooks, and reference architectures to lower the barrier to self-service

Benefits

• Hybrid working and the option to work from almost anywhere for up to 90 days per year
• £500 Remote working budget to set up your home office space
• $1,000 Learning & Development budget
• 25 days of annual leave plus bank holidays
• An extra day for your birthday
• 16 weeks fully-paid parental leave
• Private Health Insurance (Vitality)
• Full access to Spill Mental Health Support
• Life Assurance covering 4 times salary
• Cycle to Work Scheme