Staff MLOps Learning EngineerWe are looking for an MLOps/GenAIOps Engineer to own the end-to-end machine learning operations lifecycle for our US based client. This role is 100% REMOTE.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 : - 12+ years of software/data engineering experience with at least 5 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.- Robust 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). (ref:hirist.tech)

📌 Staff MLOps Learning Engineer - AWS SageMaker (India)
🏢 The HRism
📍 India