ML Ops Engineer

At Zelis, we Get Stuff Done. So, let’s get to it!

A Little About Us

Zelis is modernizing the healthcare financial experience across payers, providers, and healthcare consumers. We serve more than 750 payers, including the top five national health plans, regional health plans, TPAs and millions of healthcare providers and consumers across our platform of solutions. Zelis sees across the system to identify, optimize, and solve problems holistically with technology built by healthcare experts – driving real, measurable results for clients.

A Little About You

You bring a unique blend of personality and professional expertise to your work, inspiring others with your passion and dedication. Your career is a testament to your diverse experiences, community involvement, and the valuable lessons you've learned along the way. You are more than just your resume; you are a reflection of your achievements, the knowledge you've gained, and the personal interests that shape who you are.

Essential Duties & Functions

• Build and maintain monitoring infrastructure for conventional machine learning models, with capabilities for performance tracking, drift detection, and alerting.

• Research, evaluate, and implement monitoring strategies and tools for Generative AI systems, including LLMs and Agentic AI architectures.

• Collaborate with ML Engineers, Data Scientists, and DevOps teams to deploy, manage, and monitor models in production.

• Develop and support scalable, secure, and automated data pipelines using Snowflake, SQL, and Python for training, serving, and monitoring ML and GenAI models.

• Leverage AutoML tools and frameworks (e.g., MLflow, Kubeflow, SageMaker Autopilot) to streamline experimentation and deployment.

• Design dashboards and reporting systems to visualize model health metrics and surface key operational insights.

• Ensure auditability, reproducibility, and compliance for model performance and data flow in production environments, with consideration for regulatory standards like GDPR and HIPAA.

• Maintain CI/CD workflows and version-controlled codebases (e.g., Git) for ML infrastructure and pipelines.

• Utilize containerization and orchestration technologies (e.g., Docker) to manage scalable ML infrastructure.

• Leverage tools such as Streamlit and Python visualization libraries to present insights from model and data monitoring.

• Perform root cause analyses on model degradation or data quality issues, and proactively implement improvements.

• Stay current on industry developments related to ML observability, model governance, responsible GenAI practices, and AI security.

• Contribute to analytics projects and data engineering initiatives as needed.

• Provide off-hours support for critical deployments or urgent data/model issues.

Experience, Qualifications, Knowledge and Skills

• 2–5 years of experience in ML Ops, ML Engineering, or a related role with a focus on production-level model monitoring, automation, and deployment.

• Strong experience with ML observability tools or custom-built monitoring systems.

• Experience with monitoring LLMs and Generative AI models, including prompt evaluation, hallucination tracking, and agent behavior auditing.

• Experience in deploying and managing ML workloads using containerization and orchestration platforms such as Docker, Kubernetes, Kubeflow, or TensorFlow Extended.

• Familiarity with AutoML pipelines and workflow management tools (e.g., MLflow, SageMaker Autopilot).

• Experience working in cloud environments, preferably AWS (e.g., SageMaker, S3, Lambda, ECS/EKS).

• Understanding of ML lifecycle tools (e.g., MLflow, SageMaker Pipelines) and CI/CD practices.

• Strong security and compliance awareness, particularly related to model/data governance (e.g., HIPAA, GDPR).

• Proficiency in Python and key data libraries (Pandas, Numpy, Matplotlib, etc.).

• Advanced SQL skills and experience with Snowflake or similar data warehousing platforms.

• Proficiency with version control (Git) and agile development methodologies.

• Strong collaboration and communication skills, with the ability to explain technical issues to both technical and non-technical stakeholders.

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field—or equivalent industry experience.

• Domain experience in healthcare data (claims, payments) is preferred.

Physical Demands:

• Sedentary work - Exerting up to 10 pounds of force occasionally, and/or a negligible amount of force frequently to lift, carry, push, pull or otherwise move objects in daily work use (laptop, monitors, et. al). Sedentary work involves sitting most of the time. Use of keyboards (typing) and exposure to computer screens occurs daily. Pleasant work environment in office locations with occasional noise or dust.

• The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• While performing the duties of this job, the employee is regularly required to stand; walk; sit; use hands; reach with hands and arms; think; and talk or hear (multi-channel, two way communication during work hours is required).