Senior Machine Learning Operations Engineer II – AI Native

This is a fully remote position, open to applicants in United States.

📋 Description

• Pipeline Automation: Create, implement, and oversee automated CI/CD and Continuous Training (CT) pipelines for the development, evaluation, and delivery of machine learning models.

• Model Deployment: Package, deploy, and scale machine learning models as highly available microservices or batch processing workflows.

• Observability & Monitoring: Develop comprehensive logging, alerting, and monitoring solutions to assess model inference performance, system latency, resource utilization, data drift, and concept drift.

• Infrastructure Management: Set up and enhance cloud-based ML infrastructure (including GPU/CPU computing clusters) using Infrastructure as Code (IaC) methodologies.

• Cross-Functional Collaboration: Collaborate closely with product development teams to promote infrastructure adoption and drive efficiency improvements through SDK/API development, automation, and effective ML system maintenance.

• Governance & Compliance: Establish rigorous lineage tracking for data, code, and model artifacts to ensure compliance, reproducibility, and security throughout the entire ML lifecycle.

• Data Infrastructure & Tooling: Partner with data engineering to enhance the data ecosystem, ensuring robust, scalable pipelines for experimentation and ML (including streaming tools like Kafka and Flink for low-latency online inference).

• Thought Leadership: Serve as a mentor and thought leader, contributing to the establishment of best practices in machine learning engineering, scalable ML service operations, and agentic AI (AI-Native) methodologies.

⛳️ Requirements

• Professional Experience: Over 5 years of experience in software engineering, DevOps, or data engineering, with a minimum of 2 years focused on building and maintaining MLOps infrastructure.

• Programming Mastery: Exceptional proficiency in Python, with extensive knowledge of software engineering best practices (unit testing, modular design, version control using Git).

• Orchestration & Containerization: Demonstrated experience with containerization (Docker) and container orchestration platforms, particularly Kubernetes (EKS, GKE, or native clusters), along with familiarity with tools like FastAPI.

• MLOps and Datastore Tooling: Solid understanding of specialized ML lifecycle and data processing tools and platforms such as MLflow, Kubeflow, SparkML, Synapse ML, SQL, Spark/PySpark, dbt, and Airflow.

• Cloud Foundations: Hands-on experience working within a major cloud ecosystem—e.g., AWS, GCP, Databricks—with a solid understanding of cloud networking, security, and storage tiers.

• Strong communication and project leadership abilities, with the capacity to influence cross-functional teams.

• Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, or a related quantitative discipline.

🏝️ Benefits

• Competitive pay and benefits.

• Medical, dental, vision, life, and disability insurance plans (100% covered for US employees). Supplemental plans for medical and dental are available for Canadian employees.

• 401(k) plan with a company matching program in the US and RRSP with DPSP plan for Canadian employees.

• Employee Assistance Program (EAP) for mental wellness.

• Flexible PTO and 12 company-wide days off throughout the year.

• Learning & Development programs.

• Equipment, tools, and reimbursement support for a productive remote work environment.

• Free Life360 Platinum Membership for your preferred circle.