Sr. MLOps/GenAI Ops Engineer

ELLKAY started out providing connectivity solutions to laboratories and within a few years, grew to also provide data management solutions to ambulatory organizations. ELLKAY is now a trusted data management partner in five healthcare segments. ELLKAY’s solutions continue to serve laboratories and ambulatory practices and have expanded to empower hospitals and health systems, healthcare IT vendors, ambulatory practices, health plans, and other healthcare organizations with cutting-edge technologies and solutions that drive their growth and interoperability strategies.
Today, ELLKAY remains true to our core values, building strong partner relationships and offering unparalleled service and support while providing innovative, scalable solutions to the challenges our customers face in today’s data-rich world.
ELLKAY's experience, customer-focused approach, and reputation for innovation, speed, and accuracy differentiate ELLKAY as a premier partner for your interoperability needs and data management strategy.
Job Description
Ellkay is looking for an MLOps/GenAIOps Engineer to own the end-to-end machine learning operations lifecycle. This role focuses on automating model training, evaluation, deployment, and monitoring workflows while partnering closely with our Full Stack AI/ML Engineers to ensure seamless production deployment of foundation models and classical ML systems. You'll build the infrastructure that enables rapid, safe iteration on AI/ML solutions that impact patient care.
Essential Duties & Responsibilities
AWS SageMaker Operations & Orchestration
Design and implement SageMaker Pipelines with complex DAG authoring for end-to-end ML workflows including data preprocessing, training, evaluation, and deployment
Configure and optimize SageMaker Processing Jobs, Training Jobs (PyTorch/HuggingFace containers), and Managed Spot Instances for cost-effective model training
Manage SageMaker Model Registry with versioning, lineage tracking, and approval workflows for model governance
Implement sophisticated endpoint deployment strategies including blue/green traffic routing, canary deployments, and A/B testing configurations
Configure and maintain SageMaker Model Monitor for continuous data quality monitoring, model quality assessment, bias detection, and feature drift alerting
GenAI Model Lifecycle Management
Build operational frameworks for Amazon Bedrock model deployments including provisioned throughput management, guardrail configuration, and usage monitoring
Implement automated evaluation pipelines for foundation model outputs with quality gates and human-in-the-loop review workflows
Design prompt versioning systems integrated with model registry for reproducible GenAI deployments
Monitor GenAI model performance including latency, cost per inference, hallucination rates, and guardrail trigger metrics
Model Lifecycle Automation
Develop automated retraining trigger systems based on data drift, performance degradation, scheduled intervals, or manual triggers
Implement evaluation gates as code with configurable thresholds for metrics (F1, precision, recall, AUC, etc.) before model promotion
Build automated model promotion workflows from dev → staging → prod with approval gates and rollback capabilities
Design rollback automation with traffic shifting strategies to quickly revert to previous model versions upon performance degradation
Training Data Engineering
Build AWS Glue ETL pipelines for training corpus assembly, transformation, and feature engineering at scale
Design dataset versioning systems using S3 + manifest files ensuring reproducibility and lineage tracking across model training runs
Establish reproducibility patterns including seed management, environment pinning, and deterministic data splitting
Container Engineering & Registry Management
Build optimized Docker containers for training and inference workloads with multi-stage builds, layer caching, and security scanning
Manage Amazon ECR repositories with lifecycle policies, image scanning, and vulnerability remediation workflows
Implement bring-your-own-container (BYOC) patterns for SageMaker supporting custom frameworks and dependencies
Optimize container startup times and resource utilization for cost-effective inference
ML Observability & Monitoring
Design comprehensive CloudWatch dashboards for model health including accuracy, latency, throughput, error rates, and drift metrics
Implement custom CloudWatch metrics for business-specific KPIs (e.g., clinical accuracy per condition, false positive rates for critical alerts)
Build alerting systems for accuracy regression, data drift, concept drift, and model staleness with appropriate escalation paths
Create observability frameworks that integrate with AWS X-Ray for end-to-end request tracing from API call through model inference
CI/CD for ML Artifacts
Design and implement GitHub Actions or AWS CodePipeline workflows for automated ML artifact testing, validation, and deployment
Build multi-stage promotion pipelines (dev → staging → prod) with automated testing gates and manual approval checkpoints
Implement artifact versioning and lineage tracking for models, datasets, feature transformations, and deployment configurations
Create integration testing frameworks for ML APIs including performance benchmarking and regression testing
Infrastructure & Compliance
Collaborate with Full Stack AI/ML Engineers to define infrastructure requirements and translate them into production-grade AWS CDK constructs
Implement HIPAA-compliant ML pipelines with appropriate encryption, access controls, audit logging, and data residency requirements
Design multi-tenant ML infrastructure with tenant isolation, resource quotas, and cost allocation
Ensure reproducibility and auditability of all ML experiments and deployments for regulatory compliance
Qualifications
Technical Skills
AWS SageMaker Expertise: Deep hands-on experience with SageMaker Pipelines, Processing/Training Jobs, Model Registry, Endpoints, Model Monitor, and Managed Spot training
AWS Bedrock & GenAI Operations: Production experience managing Amazon Bedrock deployments including Claude family models, Titan Embeddings v2, and Bedrock Guardrails (PHI/PII detection); expertise in provisioned throughput vs on-demand capacity management, model evaluation automation, prompt versioning and registry integration, inference monitoring (latency, cost, token usage), and guardrail performance tracking; ability to build automated quality gates for foundation model outputs
AWS ML Services: Proficiency with AWS Glue ETL, Bedrock, S3, ECR, CloudWatch, and Step Functions
Container Technologies: Strong Docker skills including multi-stage builds, optimization techniques, and container orchestration
Python Programming: Proficient in Python for pipeline orchestration, data processing, and automation scripts (boto3, sagemaker SDK, pandas/polars)
CI/CD Tools: Experience with GitHub Actions, AWS CodePipeline, or similar CI/CD platforms for ML workflows
Infrastructure as Code: Familiarity with AWS CDK, CloudFormation, or Terraform for ML infrastructure provisioning
ML Fundamentals: Solid understanding of ML training concepts, evaluation metrics, hyperparameter tuning, and model validation techniques
Observability: Experience building custom metrics, dashboards, and alerting systems for production ML systems
Experience
8+ years of software/data engineering experience with at least 3 years focused on MLOps or production ML systems
Proven track record of building automated ML pipelines and deployment workflows at scale
Experience with model monitoring, drift detection, and automated retraining systems
Healthcare or regulated industry experience strongly preferred
Demonstrated ability to operationalize both classical ML and foundation model systems
Soft Skills
Strong collaboration skills to work closely with Full Stack AI/ML Engineers, data scientists, and platform teams
Systems thinking with ability to design reliable, fault-tolerant ML infrastructure
Excellent troubleshooting and debugging skills for complex distributed systems
Commitment to automation, reproducibility, and engineering excellence
Strong documentation skills for runbooks, architecture decisions, and operational procedures
Preferred Qualifications
Experience with Amazon Bedrock operational patterns and GenAI model monitoring
Familiarity with PyTorch, HuggingFace Transformers, and deep learning training workflows
Knowledge of FHIR, HL7 v2, and healthcare data standards
Experience with cost optimization strategies for ML workloads (Spot instances, autoscaling, model optimization)
Contributions to open-source MLOps tools or frameworks
AWS certifications (Machine Learning Specialty, Solutions Architect, or DevOps Engineer)
Additional Information
ELLKAY is committed to fostering a collaborative and high-performance work environment that supports innovation, teamwork, and professional growth. Most roles are designed to operate from our office locations to encourage effective collaboration and engagement across teams.

Work arrangement
No