Senior Staff ML Ops Engineer

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 or Kubeflow, including tracking experiments, packaging models, and managing deployments.

• Manage feature engineering platforms, allowing 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 improve 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).

• Help troubleshoot 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 our ML Ops architecture and strategy.

Qualifications:

• Minimum 8+ years of relative experience.

• Experience with Apache Airflow for orchestrating ML workflows.

• Hands-on experience with MLflow, Kubeflow, or similar platforms for model tracking and orchestration.

• Background in building and scaling feature engineering platforms.

• Experience with model monitoring, performance tracking, and drift detection using tools such as Evidently AI, WhyLabs.

• Experience with 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 ML/DevOps services.

• Excellent collaboration skills, with the ability to support and mentor data science teams.

Preferred Qualifications:

• Experience with Terraform or other Infrastructure as Code (IaC) tools.

• Knowledge of real-time data processing frameworks and streaming tools.

• Prior experience working in a regulated or production-critical environment.

• Familiarity with Large Language Models (LLMs) and a understanding of LLMOps practices, including prompt engineering, fine-tuning, and serving LLMs in production.

• Hands-on experience with LLMOps tools, such as:

  • LangChain, LlamaIndex, or Haystack for orchestration and retrieval-augmented generation (RAG)

  • Weights & Biases, MLflow, or ClearML for experiment tracking and versioning

  • Ray Serve, FastAPI, or vLLM for scalable LLM inference

  • Prompt Layer or TruLens for prompt versioning, testing, and monitoring

• Experience working with industry-standard feature engineering platforms/tools, such as:

  • Feast, Tecton, or Hopsworks for feature store implementations

  • Featuretools or PySpark for automated feature engineering and transformation pipelines

• Experience with Terraform or other Infrastructure as Code (IaC) tools for managing ML infrastructure.

• Understanding of data processing and streaming frameworks like Apache Kafka, Apache Flink, or Spark Streaming.

As with all roles, Medidata sets ranges based on several factors including function, level, candidate experience, and geographic location.

• The salary range for positions that will be physically based in the NYC Metro Area is $157,500 to $210,000.

• The salary range for positions that will be physically based in the California Bay Area is $166,550 to $222,000.

• The salary range for positions that will be physically based in the Boston Metro Area is $155,250 to $207,000.

• The salary range for positions that will be physically based in the Texas is $141,00 to $188,000.

Applications will be accepted on an ongoing basis until the position is filled.

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