Role & 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, setting 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

Preferred candidate profile

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)

Perks and benefits

📌 Sr. MLOps/GenAI Ops Engineer (India)
🏢 Ellkay Software
📍 India