MLOps Engineer

Ironwood Digital is lifting the curtain on the largest financial market in the world: structured finance. The $16+ trillion market is the backbone of everyday activities that empower financial freedom, from consolidating credit card debt and refinancing student loans, to buying a home and starting a small business. Ironwood Digital’s data analytics platform brings unparalleled transparency into investment performance and risk for lenders and Wall Street investors in structured products. As a data-first company, we wrangle critical loan data and build modern analytical tools that enable strategic decision-making for responsible lending. In a nutshell, we're helping prevent a repeat of the 2008 global financial crisis by offering the data and tools required to make smarter data-driven decisions resulting in a safer world for all of us. More than 400 of the largest financial institutions use Ironwood Digital for our coverage of over 100 million loans spanning mortgages, personal loans, auto, buy-now-pay-later programs, small business, and student loans. Ironwood Digital continues to expand coverage of new markets, adding loans monthly, and developing new technologies for the structured products universe. The Role We're looking for an MLOps Engineer to build and operate the platform that gets our machine learning and AI work into production reliably. You'll own the lifecycle tooling and infrastructure that lets data science and engineering teams train, track, deploy, and monitor models without reinventing the wheel each time. This is a hands-on, senior-individual-contributor role: you'll set technical direction in your area and mentor less-experienced engineers, while spending most of your time building. You Will Build and operate the ML lifecycle platform. Own the tooling that makes model development reproducible and production-ready, with MLflow (or comparable systems) at the center: experiment tracking, model registry, artifact and metadata management, and versioned, repeatable training and inference pipelines. Own CI/CD and deployment for ML workloads. Build automated pipelines that move models from notebook to production safely, including packaging, containerization, automated testing and validation, staged rollouts, and rollback. Make models observable and reliable in production. Stand up monitoring for model and service health, including latency, drift, data-quality, and cost signals, with alerting and clear runbooks so issues surface and resolve quickly. Build the cloud-native foundations. Contribute to and manage containerized workloads on Kubernetes and codify infrastructure with infrastructure-as-code tooling such as Terraform, keeping environments consistent, secure, and reproducible. Establish sensible guardrails. Implement infrastructure-level governance for ML systems, including access controls, deployment policies, and auditability, partnering with security and compliance to align with our risk and regulatory requirements. Enable and mentor the teams you support. Define repeatable patterns and shared services that reduce friction for data and application teams, provide technical guidance and mentorship to junior engineers, and contribute to the direction of Ironwood Digital's MLOps practices. You Have 4–7 years of relevant experience in platform engineering, DevOps, or MLOps, with solid experience operating systems in production. Hands-on experience with ML lifecycle tooling. You've built or operated experiment tracking, model registry, and pipeline workflows using MLflow or similar platforms (e.g., Weights & Biases, Kubeflow, SageMaker, Vertex AI Pipelines). This is core to the role. Strength in cloud-native infrastructure. You're comfortable with Kubernetes, containerized workloads, and infrastructure-as-code tools such as Terraform. CI/CD fluency. You've designed and maintained automated build, test, and deployment pipelines, ideally for ML or data workloads. Solid Python/Go skills and comfort supporting PyTorch-based production systems (deploying, serving, and operating them, not necessarily authoring the models). An operations and security mindset. You understand infrastructure security, IAM, secrets management, and operational risk, and you build with secure, reliable defaults. Clear communication and collaboration. You work well cross-functionally, can mentor and provide technical guidance, and are comfortable making pragmatic decisions in ambiguous problem spaces. Nice to Have Experience with GCP Experience with Pulumi Experience with GitHub Actions (GHA) Experience with Go Experience supporting data engineering platforms, data warehousing, or ETL/ELT operations Exposure to LLM serving runtimes (e.g., vLLM, llama.cpp) or agentic systems and Model Context Protocol (MCP) servers Familiarity with ML compiler stacks (e.g., LLVM/MLIR) Experience designing benchmarking or evaluation frameworks for ML/AI systems Familiarity with Excel Pivot Tables In good faith, our salary range for this role is $185,000–$200,000, but we are not tied to it. Final offer