Machine Learning Operations (MLOps) Engineer

OPEN OPENLANE US Inc; Carmel (IN); Remote; USD 110,000 - 165,000; Full time

Benefits offered by this job

Medical, dental, and vision benefits

401K with company match

Paid vacation and sick time

Parental leave

Employee Assistance Program

Tuition reimbursement

Volunteer day

Job summary

OPENLANE is hiring a remote MLOps Engineer to build and operate automated ML pipelines, from data ingestion to deployment and rollback. You will ensure reproducibility and scalable, cost-efficient production of models, with strong emphasis on observability and incident response.

You will collaborate with Data Scientists, Data Engineers, Software Engineers, and Infrastructure teams, while leveraging AI-assisted tooling to improve pipelines and platform tooling.

Qualifications

• 3+ years of experience in MLOps, ML engineering, SRE, DevOps, or software engineering.
• Bachelor’s degree preferred or equivalent practical experience.
• Strong Python skills and hands-on experience with a major cloud platform (AWS preferred).
• Experience with workflow orchestration, infrastructure-as-code, and CI/CD systems.
• Experience with containers and Kubernetes and monitoring tools.
• Hands-on experience with system debugging, observability, and incident response in production environments.

Responsibilities

• Design, build, and operate automated ML pipelines from ingestion to deployment and rollback.
• Implement Model CI/CD, canarys and safe release strategies.
• Monitor model performance, data drift, and latency with actionable alerts.
• Drive reproducibility via versioned code, data, features, and environments.
• Collaborate with Data Scientists, Engineers, and Infrastructure teams.

Skills

MLOps

Python

Cloud platforms

CI/CD

Site Reliability Engineering

DevOps

Software Engineering

Education

Bachelor's degree preferred or equivalent practical experience

Tools

Airflow

Kubeflow

Terraform

Docker

Kubernetes

Job description

Who We Are: At OPENLANE we make wholesale easy so our customers can be more successful. We’re a technology company building the world’s most advanced—and uncomplicated—digital marketplace for used vehicles. We’re a data company helping customers buy and sell smarter with clear, actionable insights they can understand and use. And we’re an innovation company accelerating the future of wholesale remarketing through curiosity, collaboration, and an entrepreneurial spirit.

Values

Driven Waybuilders. We pursue challenges that inspire us to build, create, and innovate. Relentless Curiosity. We seek to understand and improve our customers’ experience. Smart Risk‑Taking. We transform risk into progress through data, experience, and intuition. Fearless Ownership. We deliver what we promise and learn along the way.

Position Overview

We’re Looking For An Machine Learning Operations (MLOps) Engineer who builds and operates the pipelines, platforms, and observability that carry machine learning models from experimentation into reliable, cost-efficient production. You will join a new MLOps team within Site Reliability Engineering and help define how OPENLANE trains, ships, monitors, and safely updates models at scale. You will independently design automated ML delivery workflows, reduce operational toil, and ensure models are reproducible, monitored, and serving predictions safely in production. You Are Analytical, pragmatic, proactive, collaborative, quality-focused, and AI-forward. You treat AI as a first‑class engineering tool, critically evaluate its output, and apply strong engineering judgment to systems that must stay healthy long after the first deployment.

Core Knowledge and Expertise

You Bring Core knowledge of system architecture, distributed systems, cloud‑native design, networking, and agile development methodologies (Scrum, Kanban). Functional expertise in designing end‑to‑end ML pipelines (data ingestion, training, validation, deployment), including requirements for latency, redundancy, scalability, and error handling. Technical expertise in CI/CD for ML, infrastructure‑as‑code, containerization and orchestration, and metrics, monitoring, and alerting toolsets. Working understanding of the model lifecycle: experiment tracking and lineage, model registries, model drift, data quality, and retraining triggers, with enough ML fluency to diagnose model issues alongside data scientists. Strong Python skills and the ability to write clean, well‑tested, production‑ready code.

Design and Build Automation

You Own Design, build, and operation of automated ML pipelines covering data ingestion, training, validation, deployment, and rollback. Model CI/CD, including automated testing and canary and rollback strategies for safe releases. Monitoring and observability for model performance, data drift, inference latency, and availability, with actionable alerting and incident response. Reproducibility of model workloads through versioned code, data, features, and environments. Identification and reduction of operational toil through automation, standardization, and reusable ML platform components. Documentation, testing, and debugging of pipelines and serving infrastructure, maintaining high code and design standards.

Execution and Impact

You Drive Execution: Translate data science and product needs into scalable, automated ML delivery designs, and implement technical roadmap projects for the MLOps platform. Results: Shorten time‑to‑production for models while improving reliability, minimizing failure risk, and optimizing the cost, scaling, and utilization of training and inference workloads in partnership with Infrastructure teams.

Team and Mentorship

Team: Support hiring, knowledge sharing, and mentorship as the MLOps practice grows.

AI Utilization

How You’ll Use AI in This Role: AI‑assisted coding, refactoring, and test generation for pipelines and platform tooling. AI‑supported debugging and root‑cause analysis of pipeline failures and model degradation. AI‑assisted documentation and runbooks to reduce repetitive work and increase engineering leverage.

Collaboration and Work Environment

Who You Will Work With: Reporting to the Director, Site Reliability Engineering, this role collaborates daily with Data Scientists, Data Engineers, Software Engineers, Infrastructure, Security, Product Owners, and SRE teams across the organization. Where You Work: Your work is performed as a Remote employee, with occasional team alignment meetings as needed.

Must Haves

• 3+ years of experience in MLOps, ML engineering, Site Reliability Engineering, DevOps, or Software Engineering, including experience building or operating ML pipelines in production.
• Bachelor’s degree preferred or equivalent practical experience.
• Strong Python skills and hands‑on experience with a major cloud platform (AWS preferred).
• Experience with workflow orchestration (e.g., Airflow, Kubeflow, Step Functions), infrastructure‑as‑code (e.g., Terraform), and CI/CD systems.
• Experience with containers and Kubernetes (Docker, EKS preferred).
• Familiarity with model serving frameworks (e.g., TorchServe, TF Serving, Triton, BentoML, SageMaker endpoints) and monitoring tools (e.g., Prometheus, Grafana, Evidently AI).
• Hands‑on experience with system debugging, observability, and incident response in production environments.
• Actively uses AI development tools and can critically evaluate AI outputs.

Nice to Haves

• Experience with ML platforms and tooling such as SageMaker, MLflow, Databricks, or Vertex AI (GCP).
• Experience with feature stores, model registries, or LLM/GenAI deployment and evaluation (LLMOps).
• Experience with GPU workload scheduling and inference cost optimization.
• Experience interviewing candidates, conducting technical debriefs, and mentoring junior engineers or interns.
• Proven ability to acquire deep domain and architectural knowledge within 6 months of joining a team.

What We Offer

• Competitive pay
• Medical, dental, and vision benefits with employer HSA contributions (US) and FSA options (US)
• Immediately vested 401K (US) or RRSP (Canada) with company match
• Paid Vacation, Personal, and Sick Time
• Paid maternity and paternity leave (US)
• Employer‑paid short‑term disability, long‑term disability, life insurance, and AD&D (US)
• Robust Employee Assistance Program
• Employer paid Leap into Service Day to volunteer
• Tuition Reimbursement for eligible programs
• Opportunities to expand your skill set and share your knowledge across a publicly traded, global organization

About Our Platform

OPENLANE makes wholesale easy so our customers can be more successful. Our end‑to‑end platform serves the remarketing needs of the world’s largest OEMs, dealers, fleet operators, rental companies and financial institutions. We’re a technology company delivering next generation tools to accelerate and simplify remarketing. We’re an analytics company leveraging data to inform and empower our customers with clear, actionable insights. And we’re an auction company powering the world’s most advanced digital used vehicle marketplaces. In 2021, OPENLANE sold nearly 2.6 million vehicles valued at over $40 billion through our auctions and generated approximately $2.3 billion in revenue. Our integrated digital marketplaces and vehicle logistics centers reduce risk, improve transparency and streamline the experience for customers in about 75 countries. OPENLANE continues to make investments in innovation, strategic acquisitions and integrations to better serve our customers.

Equal Opportunity

OPENLANE is an equal opportunity employer and a drug‑free workplace.