Senior Staff ML Ops Engineer

About the Role

Key Responsibilities

• Design and implement ML pipelines using Apache Airflow to automate data preparation, model training, evaluation, and deployment.
• Support model lifecycle management using tools like MLflow and/or Kubeflow, including tracking experiments, packaging models, and managing deployments.
• Set up and maintain feature engineering platforms, enabling reuse, versioning, and scalability of features across ML workflows.
• Implement model monitoring and drift detection using platforms such as Evidently AI or similar solutions to ensure high model performance and reliability in production.
• Collaborate with data science teams to streamline the transition of models from research to production.
• Ensure systems are scalable, maintainable, and well-documented, following best practices in DevOps, CI/CD, and Infrastructure as Code (IaC).
• Assist in troubleshooting and debugging model and pipeline issues in production environments.
• Advocate for and implement observability, logging, and alerting in ML systems.
• Contribute to the continuous improvement of ML Ops architecture and strategy.

Requirements

• Minimum 8+ years of related experience.
• Proven experience with Apache Airflow for orchestrating ML workflows.
• Hands-on experience with MLflow, Kubeflow, or similar platforms for model tracking and orchestration.
• Strong background in building and scaling feature engineering platforms.
• Experience with model monitoring, performance tracking, and drift detection using tools such as Evidently AI, WhyLabs.
• Solid understanding of ML lifecycle management, CI/CD pipelines, and containerization technologies like Docker and Kubernetes.
• Proficient in Python, with knowledge of ML and data engineering libraries (e.g., pandas, scikit-learn, Spark).
• Familiarity with cloud platforms (AWS, GCP, or Azure) and relevant ML/DevOps services.
• Excellent collaboration and communication skills, with the ability to support and mentor data science teams.
• Experience with Terraform or other Infrastructure as Code (IaC) tools.

Nice to Have

• Knowledge of real-time data processing frameworks and streaming tools.
• Prior experience working in a highly regulated or production-critical environment.
• Familiarity with Large Language Models (LLMs) and LLMOps practices, including prompt engineering, fine-tuning, and serving LLMs in production.
• Hands-on experience with LLMOps tools, such as LangChain, LlamaIndex, Haystack, Weights & Biases, MLflow, ClearML, Ray Serve, FastAPI, vLLM, Prompt Layer, TruLens.
• Experience working with industry-standard feature engineering platforms/tools, such as Feast, Tecton, Hopsworks, PySpark.
• Understanding of data processing and streaming frameworks like Apache Kafka, Apache Flink, or Spark Streaming.

Qualifications

• Educational background is not explicitly specified, but extensive experience (8+ years) is required.

Benefits & Perks

• Best-in-class benefits including medical, dental, life and disability insurance.
• 401(k) matching.
• Flexible paid time off.
• 10 paid holidays per year.