MLOps Engineer

Role: ML Ops Engineer

Duration: Full time

Location: Remote

Overview

Seeking an experienced ML Ops Engineer to build and operate scalable ML infrastructure supporting predictive models, NLP, and LLM applications in a healthcare environment. The role focuses on automation, deployment, monitoring, governance, and HIPAA-compliant ML operations.

Key Responsibilities

• Build automated ML training, evaluation, and deployment pipelines using MLflow, Databricks Workflows, and AWS SageMaker.
• Manage model serving and orchestration using Docker, Kubernetes/EKS, and APIs.
• Implement model monitoring for drift, performance, latency, and pipeline failures.
• Support feature stores, data/version management, and training-to-production consistency.
• Manage LLM integrations including Bedrock, prompt versioning, logging, guardrails, and cost controls.
• Build CI/CD and Continuous Training (CT) workflows.
• Automate infrastructure using Terraform/CloudFormation and manage security, secrets, and access controls.
• Partner with Data Scientists and Data Engineers and help establish MLOps standards and best practices.

Requirements

• 7+ years in DevOps, Data Engineering, or ML Engineering, including 4+ years in MLOps.
• Proven experience operating production ML systems, including deployment, monitoring, and lifecycle management.
• Healthcare or regulated-data experience; HIPAA knowledge required.
• Strong Docker, Kubernetes/EKS, Terraform, Python, and Bash skills.
• Deep experience with MLflow, Databricks, Spark, Unity Catalog, and AWS SageMaker.
• Strong AWS experience including S3, Lambda, API Gateway, Bedrock, Glue, Step Functions, Kinesis, and Secrets Manager.
• Experience with Kafka/Kinesis, feature stores, LLM/RAG infrastructure, and ML monitoring.
• Strong communication and collaboration skills.