{"id":3669328,"url":"https://alion.io/job/the-hrism-staff-mlops-learning-engineer-aws-sagemaker","title":"Staff MLOps Learning Engineer - AWS SageMaker","company":{"id":4503273,"name":"The HRism","domain":"thehrism.com","url":"https://alion.io/company/the-hrism","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Bengaluru, India","Pune, India"],"countries":["IN"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS CDK","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Boto3","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"CloudFormation","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Hallucination","optional":false},{"name":"HIPAA","optional":false},{"name":"Hugging Face","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Terraform","optional":false},{"name":"FHIR","optional":true},{"name":"HL7","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-11-03T07:11:32Z","employer_posted_date":null,"last_verified_at":"2026-11-03T07:11:32Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"Staff MLOps Learning Engineer\n\nWe 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.\n\nThis 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.\n\nEssential Duties & Responsibilities:\n\nAWS SageMaker Operations & Orchestration:\n\n- Design and implement SageMaker Pipelines with complex DAG authoring for end-to-end ML workflows including data preprocessing, training, evaluation, and deployment.\n\n- Configure and optimize SageMaker Processing Jobs, Training Jobs (PyTorch/HuggingFace containers), and Managed Spot Instances for cost-effective model training.\n\n- Manage SageMaker Model Registry with versioning, lineage tracking, and approval workflows for model governance.\n\n- Implement sophisticated endpoint deployment strategies including blue/green traffic routing, canary deployments, and A/B testing configurations.\n\n- Configure and maintain SageMaker Model Monitor for continuous data quality monitoring, model quality assessment, bias detection, and feature drift alerting.\n\nGenAI Model Lifecycle Management:\n\n- Build operational frameworks for Amazon Bedrock model deployments including provisioned throughput management, guardrail configuration, and usage monitoring.\n\n- Implement automated evaluation pipelines for foundation model outputs with quality gates and human-in-the-loop review workflows.\n\n- Design prompt versioning systems integrated with model registry for reproducible GenAI deployments.\n\n- Monitor GenAI model performance including latency, cost per inference, hallucination rates, and guardrail trigger metrics.\n\nModel Lifecycle Automation:\n\n- Develop automated retraining trigger systems based on data drift, performance degradation, scheduled intervals, or manual triggers.\n\n- Implement evaluation gates as code with configurable thresholds for metrics (F1, precision, recall, AUC, etc.) before model promotion.\n\n- Build automated model promotion workflows from dev - staging - prod with approval gates and rollback capabilities.\n\n- Design rollback automation with traffic shifting strategies to quickly revert to previous model versions upon performance degradation.\n\nTraining Data Engineering:\n\n- Build AWS Glue ETL pipelines for training corpus assembly, transformation, and feature engineering at scale.\n\n- Design dataset versioning systems using S3 + manifest files ensuring reproducibility and lineage tracking across model training runs.\n\n- Establish reproducibility patterns including seed management, environment pinning, and deterministic data splitting.\n\nContainer Engineering & Registry Management:\n\n- Build optimized Docker containers for training and inference workloads with multi-stage builds, layer caching, and security scanning.\n\n- Manage Amazon ECR repositories with lifecycle policies, image scanning, and vulnerability remediation workflows.\n\n- Implement bring-your-own-container (BYOC) patterns for SageMaker supporting custom frameworks and dependencies.\n\n- Optimize container startup times and resource utilization for cost-effective inference.\n\nML Observability & Monitoring:\n\n- Design comprehensive CloudWatch dashboards for model health including accuracy, latency, throughput, error rates, and drift metrics.\n\n- Implement custom CloudWatch metrics for business-specific KPIs (e.g., clinical accuracy per condition, false positive rates for critical alerts).\n\n- Build alerting systems for accuracy regression, data drift, concept drift, and model staleness with appropriate escalation paths.\n\n- Create observability frameworks that integrate with AWS X-Ray for end-to-end request tracing from API call through model inference.\n\nCI/CD for ML Artifacts:\n\n- Design and implement GitHub Actions or AWS CodePipeline workflows for automated ML artifact testing, validation, and deployment.\n\n- Build multi-stage promotion pipelines (dev - staging - prod) with automated testing gates and manual approval checkpoints.\n\n- Implement artifact versioning and lineage tracking for models, datasets, feature transformations, and deployment configurations.\n\n- Create integration testing frameworks for ML APIs including performance benchmarking and regression testing.\n\nInfrastructure & Compliance:\n\n- Collaborate with Full Stack AI/ML Engineers to define infrastructure requirements and translate them into production-grade AWS CDK constructs.\n\n- Implement HIPAA-compliant ML pipelines with appropriate encryption, access controls, audit logging, and data residency requirements.\n\n- Design multi-tenant ML infrastructure with tenant isolation, resource quotas, and cost allocation.\n\n- Ensure reproducibility and auditability of all ML experiments and deployments for regulatory compliance.\n\nQualifications:\n\nTechnical Skills:\n\n- AWS SageMaker Expertise: Deep hands-on experience with SageMaker Pipelines, Processing/Training Jobs, Model Registry, Endpoints, Model Monitor, and Managed Spot training.\n\n- 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.\n\n- AWS ML Services: Proficiency with AWS Glue ETL, Bedrock, S3, ECR, CloudWatch, and Step Functions.\n\n- Container Technologies: Strong Docker skills including multi-stage builds, optimization techniques, and container orchestration.\n\n- Python Programming: Proficient in Python for pipeline orchestration, data processing, and automation scripts (boto3, sagemaker SDK, pandas/polars).\n\n- CI/CD Tools: Experience with GitHub Actions, AWS CodePipeline, or similar CI/CD platforms for ML workflows.\n\n- Infrastructure as Code: Familiarity with AWS CDK, CloudFormation, or Terraform for ML infrastructure provisioning.\n\n- ML Fundamentals: Solid understanding of ML training concepts, evaluation metrics, hyperparameter tuning, and model validation techniques.\n\n- Observability: Experience building custom metrics, dashboards, and alerting systems for production ML systems.\n\nExperience:\n\n- 12+ years of software/data engineering experience with at least 5 years focused on MLOps or production ML systems.\n\n- Proven track record of building automated ML pipelines and deployment workflows at scale.\n\n- Experience with model monitoring, drift detection, and automated retraining systems.\n\n- Healthcare or regulated industry experience strongly preferred.\n\n- Demonstrated ability to operationalize both classical ML and foundation model systems.\n\nSoft Skills:\n\n- Strong collaboration skills to work closely with Full Stack AI/ML Engineers, data scientists, and platform teams.\n\n- Systems thinking with ability to design reliable, fault-tolerant ML infrastructure.\n\n- Excellent troubleshooting and debugging skills for complex distributed systems.\n\n- Commitment to automation, reproducibility, and engineering excellence.\n\n- Strong documentation skills for runbooks, architecture decisions, and operational procedures.\n\nPreferred Qualifications:\n\n- Experience with Amazon Bedrock operational patterns and GenAI model monitoring.\n\n- Familiarity with PyTorch, HuggingFace Transformers, and deep learning training workflows.\n\n- Knowledge of FHIR, HL7 v2, and healthcare data standards.\n\n- Experience with cost optimization strategies for ML workloads (Spot instances, autoscaling, model optimization).\n\n- Contributions to open-source MLOps tools or frameworks.\n\n- AWS certifications (Machine Learning Specialty, Solutions Architect, or DevOps Engineer).\n\nSkills\nMLOps, AWS SageMaker, Generative AI, Python, AWS, Machine Learning, CI/CD, IAC 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