Senior Machine Learning Ops Engineer

Accountabilities: The Senior Machine Learning Ops Engineer will be responsible for designing, implementing, and maintaining scalable machine learning infrastructure that supports production AI initiatives. This role requires strong engineering expertise, operational ownership, and the ability to collaborate across technical teams to create reliable and efficient ML platforms.

Design, deploy, and maintain scalable ML infrastructure supporting model training, batch processing, and real-time inference workloads.

Build and manage cloud-based infrastructure and services using AWS, Snowflake, and related platforms through Infrastructure-as-Code practices.

Develop and maintain containerized ML deployment solutions using Docker, FastAPI, and modern software delivery patterns.

Create and improve CI/CD pipelines, automation frameworks, testing processes, and deployment standards for machine learning systems.

Partner with Data Science and Data Engineering teams to productionize models and accelerate machine learning delivery.

Establish monitoring and observability frameworks, including model performance tracking, drift detection, data quality monitoring, and automated alerting.

Improve platform reliability, scalability, security, governance, and operational efficiency across ML workflows.

Support architecture decisions, engineering standards, and best practices for enterprise ML platforms.

Document technical architecture, deployment processes, and operational procedures to ensure maintainability and knowledge sharing.

Contribute to the development of reusable ML infrastructure components and data products.

Support both batch and low-latency inference workflows while optimizing system performance.

Help define the future direction of machine learning operations and platform capabilities.

Requirements:

The ideal candidate is a technically strong engineer with experience building production machine learning systems, cloud infrastructure, and scalable software platforms. They should be comfortable operating independently while collaborating with cross-functional teams to solve complex technical challenges.

Bachelor’s degree in Computer Science, Data Engineering, or a related technical field; advanced degree preferred.

6+ years of experience in MLOps, platform engineering, DevOps, data engineering, or related infrastructure roles.

3+ years of hands-on experience working with AWS cloud infrastructure.

Strong Python engineering skills, including API development, automation, and backend service development.

Experience building and operating production machine learning systems.

Strong knowledge of Docker, containerized application deployment, and modern deployment practices.

Experience with Kubernetes, ECS, EKS, or similar container orchestration platforms.

Experience managing Infrastructure-as-Code projects using tools such as Terraform, OpenTofu, or CloudFormation.

Strong SQL skills and experience with modern data warehouse platforms such as Snowflake, Databricks, or BigQuery.

Experience implementing CI/CD workflows and software engineering best practices.

Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect.

Experience with testing frameworks such as pytest, including unit, integration, and end-to-end testing approaches.

Strong understanding of Bash and Unix-based environments.

Experience with backend frameworks such as FastAPI, Flask, or Django.

Knowledge of ML observability and experiment tracking tools such as MLflow, Arize, Evidently, WhyLabs, or Monte Carlo is a plus.

Experience designing feature stores or reusable ML data products is preferred.

Experience supporting Generative AI, LLM deployment workflows, financial services, fintech, or regulated industries is a plus.

Strong communication skills with the ability to collaborate effectively across Data Science, Data Engineering, Product, and technical teams.

Ability to manage multiple priorities, work independently, and thrive in a fast-paced environment.

Benefits:

Competitive salary range of approximately $150,500 to $173,000 annually, depending on location, skills, experience, and qualifications.

Medical, dental, and vision insurance coverage.

401(k) retirement plan with company match.

Paid holidays, vacation time, sick days, and volunteer time off.

12 weeks of paid parental leave.

Pre-tax transit benefits.

Company-paid life insurance.

Voluntary benefits options.

Discounted pet health insurance.

Wellness incentive programs.

Employee mentorship and leadership development opportunities.

Team-oriented culture with opportunities for professional growth.

Remote work flexibility.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether? 

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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