Elliptic
Financial Crime
StaffMLOpsEngineer
“Staff MLOps Engineer at Elliptic. Skills: MLOps platform development, ML infrastructure development, Model registry, ML pipeline orchestration. Define target-state MLOps architecture. Produce architecture decision records”
What You'll Achieve.
Define and build Elliptic's Enterprise MLOps platform; Create the unified platform layer that ties training, deployment, monitoring, and governance together; Enforce governance with enough rigour; Remain flexible enough to avoid slowing down research teams; Ship production-grade platform capabilities; Lower the barrier to self-service; Bring it to a state where others could operate and extend it
Industry & Context.
Making decisions with incomplete information; Creating structure where none exists; Remaining open to changing course when better information arrives; Pressure-test your own designs
Option to work from almost anywhere for up to 90 days per year
What They're Looking For.
Must Have
Built MLOps platforms or ML infrastructure from the ground up, Operated in a regulated industry, Experience building ML infrastructure to meet regulatory demands, Comfortable operating in ambiguity, Making decisions with incomplete information, Creating structure where none exists, Production engineering quality, Write production-grade code, Systems are tested, Systems are observable, Systems are documented, Systems designed for others to operate
Nice to Have
Familiarity with model risk management frameworks, Ability to connect governance practices to regulatory expectations, Experience working simultaneously with research-oriented ML teams and production-oriented engineering teams, Understanding how their needs diverge, Infrastructure-as-code fluency (Terraform), Experience with ClickHouse or similar OLAP engines for low-latency ML feature serving, Blockchain or crypto domain knowledge, Experience working in fraud detection and modelling, Contributions to open-source MLOps tooling
What You'll Do.
Define target-state MLOps architecture
Produce architecture decision records
Make build-vs-buy-vs-stop recommendations
Improve existing model registry
Close identified gaps
Build model training pipelines
Build serving infrastructure
Instrument observability across ML lifecycle
Integrate with existing observability stack
Onboard data scientists and ML engineers
Write reference architectures
How You'll Work.
Team & Collaboration
Work with InfoSec; Work directly with infrastructure engineers; Work directly with data scientists; Work with ML engineers
Communication Scope
Influence through clarity; Influence through evidence; Influence through quality of work
Process & Methodology
Create structure where none exists
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