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Zelis · 15 hours ago

ML Ops Engineer

Middlesex County, NJ

Full-time

Onsite

Entry, Mid Level

$127K/yr - $161K/yr

2+ years exp

Zelis is modernizing the healthcare financial experience across payers, providers, and healthcare consumers. The MLOps Engineer will support Generative AI, traditional ML, and Advanced Analytics initiatives, focusing on automation, security, and scalability across the ML lifecycle.

FinanceFinTechHealth Care

No H1B

Responsibilities

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

Qualification

ML OpsPythonSQLContainerizationAutoML toolsCloud environmentsML observabilityData pipelinesHealthcare data experienceCollaboration skillsCommunication skills

Required

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