MLOps Engineer

dv01

1d • $185,000 - $200,000 • Remote

About The Position

dv01 is seeking an MLOps Engineer to build and operate the platform that reliably gets machine learning and AI work into production. This role will own the lifecycle tooling and infrastructure that enables data science and engineering teams to train, track, deploy, and monitor models efficiently. It is a hands-on, senior-individual-contributor position where the engineer will set technical direction and mentor others while focusing on building.

dv01 operates in the structured finance market, a $16+ trillion industry. Their data analytics platform provides transparency into investment performance and risk for lenders and Wall Street investors. The company wrangles loan data and builds analytical tools to support strategic decision-making for responsible lending, aiming to prevent financial crises by promoting data-driven decisions. Over 400 financial institutions utilize dv01's platform, covering more than 100 million loans across various financial products.

Requirements

• 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, including building or operating experiment tracking, model registry, and pipeline workflows using MLflow or similar platforms (e.g., Weights & Biases, Kubeflow, SageMaker, Vertex AI Pipelines).
• Comfort with cloud-native infrastructure, including Kubernetes, containerized workloads, and infrastructure-as-code tools such as Terraform.
• CI/CD fluency, with experience designing and maintaining 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).
• An operations and security mindset, understanding infrastructure security, IAM, secrets management, and operational risk.
• Clear communication and collaboration skills, ability to work cross-functionally, mentor junior engineers, and make pragmatic decisions in ambiguous problem spaces.

Nice To Haves

• 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

Responsibilities

• Build and operate the ML lifecycle platform, owning the tooling for reproducible and production-ready model development using MLflow or comparable systems for experiment tracking, model registry, artifact and metadata management, and versioned training/inference pipelines.
• Own CI/CD and deployment for ML workloads, building automated pipelines for safe model movement from notebook to production, including packaging, containerization, automated testing, staged rollouts, and rollback.
• Make models observable and reliable in production by setting up monitoring for model and service health (latency, drift, data quality, cost) with alerting and runbooks.
• Build cloud-native foundations, contributing to and managing containerized workloads on Kubernetes and using infrastructure-as-code tools like Terraform for consistent, secure, and reproducible environments.
• Establish infrastructure-level governance for ML systems, including access controls, deployment policies, and auditability, in partnership with security and compliance teams.
• Enable and mentor teams by defining repeatable patterns and shared services, providing technical guidance to junior engineers, and contributing to dv01's MLOps practices.

Benefits

• Unlimited PTO
• $1,000 Learning & Development Fund
• Remote-First Environment
• Comprehensive medical, dental, and vision insurance package
• 401(k)
• $138/month for gym or fitness membership, or up to $1,650 per year through Fitness Fund for workout equipment/wellness essentials
• 16 weeks of 100% paid leave for primary caregivers
• 4 weeks of paid leave for secondary caregivers

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed