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.