Senior Machine Learning Operations Engineer II (AI Native)

Pipeline Automation

• Design, implement, and manage automated CI/CD and Continuous Training (CT) pipelines for machine learning model development, evaluation, and delivery.
• Containerize, deploy, and scale machine learning models as high-availability microservices or batch processing workflows.

Model Deployment

• Establish unified logging, alerting, and monitoring solutions to track model inference performance, system latency, resource utilization, data drift, and concept drift.
• Provision and optimize cloud-based ML infrastructure (including GPU/CPU computing clusters) utilizing Infrastructure as Code (IaC) paradigms.

Cross-Functional Collaboration

• Work intimately with product development teams to drive infrastructure adoption and efficiency gains through SDK/API development, automation, and efficient ML system maintenance.

Governance & Compliance

• Implement robust lineage tracking for data, code, and model artifacts to ensure compliance, reproducibility, and security across the entire ML lifecycle.

Data Infrastructure & Tooling

• Work with data engineering to improve 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

• Act as a mentor and thought leader, helping to define best practices in machine learning engineering, scalable ML service ops, and agentic AI (AI-Native) best practices.

Desired Experience & Qualifications

• Professional Experience: 5+ years of professional software engineering, DevOps, or data engineering experience, with at least 2 years dedicated to building and maintaining MLOps infrastructure.
• Programming Mastery: Strong proficiency in Python, including deep familiarity with software engineering best practices (unit testing, modular design, version control via Git).
• Orchestration & Containerization: In addition to hands-on experience with containerization (Docker) and container orchestration platforms, specifically Kubernetes (EKS, GKE, or native clusters), experience with related tools like FastAPI.
• MLOps and Datastore Tooling: Proven familiarity with specialized ML lifecycle and data processing tools and platforms such as MLflow, Kubeflow, SparkML, Synapse ML, SQL, Spark/PySpark, dbt, and Airflow.
• Cloud Foundations: Practical experience operating within a major cloud ecosystem—e.g., AWS, GCP, Databricks—with a clear grasp of cloud networking, security, and storage tiers.
• Strong communication and project leadership skills, with the ability to influence cross-functional teams.
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, or a closely related quantitative field.

Preferred Qualifications

• Advanced Tooling: Experience implementing and scaling production feature stores (e.g., Feast, Tecton) and model registries.
• Generative AI & LLMs: Prior experience deploying and optimizing Large Language Models (LLMs) or foundation models utilizing serving frameworks like vLLM, Triton Inference Server, or TGI.
• Infrastructure as Code: Proficient with IaC frameworks, particularly Terraform, to manage reproducible environments.
• Data Frameworks: Familiarity with distributed data computation engines such as Apache Spark, Ray, or Dask.
• Industry Certifications: Relevant cloud or architecture credentials, such as AWS Certified Machine Learning Specialty, Google Cloud Professional Machine Learning Engineer, or Certified Kubernetes Administrator (CKA).
• Experience in subscription-based products, lifecycle marketing, or user acquisition.
• Experience with geospatial data and mobile location-based services.
• Experience in the consumer technology sector, particularly within a fast-paced and sometimes ambitious development setting.

Core Expectations

• Problem-solving mindset - You structure ambiguous problems precisely before reaching for a tool, AI or otherwise
• Collaborative approach - You can explain technical tradeoffs and articulate ideas effectively, work well across teams, and value diverse perspectives
• Ownership mentality - You take responsibility for your work from design through production and beyond
• AI-native working style - You use AI tooling (Claude Code or equivalent) as a genuine development partner: delegating discrete tasks, reviewing outputs critically, and running parallel workstreams rather than hand-holding one agent at a time