MLOps/GenAIOps Engineer

Title: MLOps/GenAIOps Engineer

Location: Remote Timings: 4:00PM –12:00 AM IST (potential to change slightly 1 hour)

Duration: 6-month contract (might extend, not funded to at this time) Looking for who can join immediate joiners or 1-2 weeks.

Job Description: About the Role: We’re looking for an experienced MLOps/GenAIOps Engineer to own the end-to-end machine learning operations lifecycle for innovative healthcare AI solutions. In this role, you’ll automate and orchestrate model training, evaluation, deployment, and monitoring workflows—building production infrastructure on AWS SageMaker and Bedrock to support both foundation models and classical ML systems. You’ll partner closely with Full Stack AI/ML Engineers to enable seamless, secure, and compliant deployment of AI/ML features that make a real impact on patient care. You’ll design robust CI/CD for ML artifacts, optimize model deployment strategies (blue/green, canary, A/B testing), manage automated retraining and evaluation gates, and implement advanced monitoring for data drift, model quality, and regulatory compliance. The ideal candidate will bring deep expertise in SageMaker Pipelines, Bedrock, Docker, AWS Glue ETL, CloudWatch observability, and automation best practices—ideally with a track record supporting healthcare or other regulated industries.

Qualifications:

• 8+ years of software or data engineering, including 5+ years in MLOps or production ML
• Deep hands-on AWS SageMaker experience (Pipelines, Model Registry, Processing/Training Jobs, Endpoints, Model Monitor, Spot training)
• Proven capability with Amazon Bedrock model deployments, large language model evaluation, prompt versioning, and GenAI monitoring
• Strong Python programming and automation (boto3, sagemaker SDK, pandas/polars)
• Proficient with AWS ML services (Glue, S3, ECR, CloudWatch, Step Functions), Docker/container engineering, and CI/CD (GitHub Actions, CodePipeline)
• Experience building scalable ML pipelines, container registries, and model rollout/rollback automation
• Familiarity with infrastructure as code (AWS CDK/CloudFormation), reproducibility patterns, and security/compliance best practices (HIPAA, PHI)
• Excellent collaboration skills, systems thinking, attention to detail, and a commitment to engineering excellence
• Healthcare/regulated experience and ML observability background preferred

Nice to Have:

• Experience with FHIR/HL7, PyTorch, HuggingFace, open-source contributions, or AWS certifications (Machine Learning Specialty, Solutions Architect, or DevOps Engineer).
• All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.