Staff Product Manager, ML Foundations and GenAI

The impact you will have: As Staff MLOps Engineer, you will define and build Elliptic's Enterprise MLOps platform. Elliptic has growing ML capability across several teams, an established model registry, and a maturing model risk management practice. What is missing is the unified platform layer that ties training, deployment, monitoring, and governance together into a coherent, scalable discipline. You will be responsible for creating that layer. Your platform will serve four distinct internal consumers, each with different needs: * Product Engineering teams building customer-facing models and customer data analytical models, who need reproducible training pipelines, CI/CD for model deployment, and low-latency serving infrastructure * Intelligence Research building frontier intelligence collection, predictive pre-screening models, and behavioural pattern detection, who need rapid experimentation, GPU orchestration, and dataset versioning * InfoSec who own the model registry and model risk management framework today, and need the platform to close execution gaps in audit trails, drift monitoring, and compliance reporting * Operations who own BI, usage prediction, and revenue opportunity signalling, and need scheduled batch inference, BI integration, and pipeline reliability The platform you build must enforce governance with enough rigour to satisfy a regulated financial crime context, while remaining flexible enough to avoid slowing down research teams who need to iterate quickly. This is a role for someone who has built ML infrastructure from the ground up before, who understands that a platform succeeds only when it is adopted, and who is comfortable making build-vs-buy decisions that others will adopt and use for years. What you will do: * Define the target-state MLOps architecture for Elliptic, covering model training pipelines, serving infrastructure, monitoring, feature management, and governance, and produce the architecture decision records that inform investment decisions * Make and document build-vs-buy-vs-stop recommendations with clear cost modelling and trade-off analysis, evaluating vendors, open-source tools, and managed services against Elliptic's constraints (AWS-primary, Databricks ecosystem) * Work with InfoSec to improve the existing model registry and model risk management framework, closing identified gaps in metadata, lineage, approval workflows, and drift/bias detection * Build model training pipelines, CI/CD for ML, and serving infrastructure, working directly with a small group of infrastructure engineers to ship production-grade platform capabilities * Instrument observability across the ML lifecycle: training metrics, serving latency and throughput, data quality, and prediction drift, integrating with Elliptic's existing observability stack * Work directly with data scientists and ML engineers across all four consumer groups to onboard them onto the platform, writing documentation, runbooks, and reference architectures that lower the barrier to self-service You will be a great fit here if you: * Have built MLOps platforms or ML infrastructure from the ground up, and can speak to what worked, what didn't, and why * Have operated in a regulated industry (e.g. compliance, financial) and have hands on experience building ML infrastructure to meet those regulatory demands * Think about ML infrastructure the way the best platform engineers think about data infrastructure: as a set of foundations with internal customers whose needs must be understood and balanced * Are comfortable operating in ambiguity, making decisions with incomplete information, and creating structure where none exists, while remaining open to changing course when better information arrives * Influence through clarity, evidence, and the quality of your work rather than positional authority. You earn adoption by making the platform genuinely better than the alternative * Care about production engineering quality: you write production-grade code, your systems are tested, observable, documented, and designed for others to operate Our ideal candidate has: * 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 * A 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 * See AI as a core part of how modern engineering gets done, not a passing trend. You actively use it to think faster, prototype faster, and pressure-test your own designs, and you're excited that the bar keeps rising. * Prior experience running formal build-vs-buy evaluations with written decision records Bonus Points for: * Familiarity with model risk management frameworks and the ability to connect governance practices to regulatory expectations * Experience working simultaneously with research-oriented ML teams and production-oriented engineering teams, and 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 JOB BENEFITS > How we work: * 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 > Learning & Development: * $1,000 Learning & Development budget to use on anything (agreed with your manager) that contributes to your growth and development > Vacation/ Leave: * Holidays: 25 days of annual leave + bank holidays * An extra day for your birthday * Enhanced parental leave: we provide eligible employees, regardless of gender or whether they become a parent by birth or adoption, 16 weeks fully-paid leave and leave. > Benefits: * Private Health Insurance - we use Vitality! * Full access to Spill Mental Health Support * Life Assurance: we hope you will never need this - but our cover is for 4 times your salary to your beneficiaries * Cycle to Work Scheme

The impact you will have: As Staff AI Engineer, you will be one of the most impactful early hires in Elliptic's next stage of AI expansion. You will join at a moment when Elliptic is actively forming its approach to AI foundations: tooling decisions are being made, agentic patterns are being established, and the kernel of a centralised AI platform is being laid out. Your role is to govern the quality and coherence of those decisions before they crystallise. You will initially work across our AgentForce and Investigations & AI teams, holding the architectural bar on tooling evaluations, keeping the stack decision open and well-reasoned, and ensuring that the internal agentic patterns being developed today are genuinely inheritable by the customer-facing AI products of tomorrow. You will act as a strong advocate for AI adoption, AI technical best practices, and AI enablement across product, engineering, and development. This is a role for someone who is comfortable with ambiguity, energised by the challenge of making decisions that others will build on for years, and confident enough to hold a strong technical position without needing a team beneath them to do it. What you will do: * Serve as the architectural conscience for Elliptic's early AI decisions, evaluating our current tooling explorations (including the LangSmith ecosystem and Databricks) against the requirements of production-scale, customer-facing AI products, and producing a clear, evidence-based recommendation * Work consultatively with the Investigations & AI technical lead and AgentForce engineering to ensure that internal agentic patterns, prompt architectures, and evaluation frameworks are being designed with customer-facing scale and regulatory auditability in mind * Hold the AI stack decision open responsibly: document trade-offs, establish evaluation criteria, and prevent pragmatic local choices from defaulting the answer before the right person is in place to make it * Define and uphold engineering standards for AI systems across the organisation: model observability and tracing, prompt versioning and registry, cost governance, evaluation harnesses, and agent reliability patterns * Produce the technical foundation documents that will be a coherent architectural position, a clear view of decisions made and decisions deferred, and an honest assessment of what the architecture can accomplish You will be a great fit here if you: * Are energised by the challenge of bringing rigour to early-stage technical decisions, and understand that preventing a bad architectural choice is often more valuable than shipping a feature * Can hold a strong, well-reasoned technical position without needing formal authority to make it stick. You influence through clarity, evidence, and the quality of your thinking * Think about AI infrastructure the way the best platform engineers think about data infrastructure: as a set of foundations with internal customers whose needs must be understood and balanced * Are comfortable operating in ambiguity and working across teams without a fixed mandate, and know how to make yourself useful in a way that doesn't create dependency or territorial friction * Care about the trustworthiness of AI systems, not just their capability. Understand why explainability, auditability, and reliability matter especially in a regulated compliance context Our ideal candidate has: * Made production AI architectural decisions, including evaluation framework selection, LLM integration patterns, prompt management and versioning at scale, and model observability. You can speak to what went well, what they would do differently, and why * Worked across the boundary between internal tooling and customer-facing AI products, and understands how requirements differ across those contexts, particularly in relation to reliability, auditability, and cost * Built or significantly shaped an AI evaluation or observability framework in a production environment, and has strong opinions on what good looks like * Operated effectively without a team beneath them. As a Staff IC whose impact comes from technical leadership and cross-team influence rather than people management and team workstream prioritisation Bonus Points for: * Experience building agentic systems in a production context, including orchestration patterns, tool use, memory management, and agent reliability at scale * Familiarity with one of the major AI ecosystems, such as LangSmith, MLflow, or Databricks ML * Having navigated a transition from a scrappy, point-to-point AI integration to a well-engineered, reusable AI platform. An understanding of the organisational as well as technical challenges that transition involves * An interest in the crypto ecosystem and the mission of making digital assets safer and more accessible JOB BENEFITS > How we work: * 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 > Learning & Development: * $1,000 Learning & Development budget to use on anything (agreed with your manager) that contributes to your growth and development > Vacation/ Leave: * Holidays: 25 days of annual leave + bank holidays * An extra day for your birthday * Enhanced parental leave: we provide eligible employees, regardless of gender or whether they become a parent by birth or adoption, 16 weeks fully-paid leave and leave. > Benefits: * Private Health Insurance - we use Vitality! * Full access to Spill Mental Health Support * Life Assurance: we hope you will never need this - but our cover is for 4 times your salary to your beneficiaries * Cycle to Work Scheme