AI/ML Solutions Architect - HealthIT (AWS Cloud)

Job Title - AI/ML Solutions Architect - HealthIT (AWS Cloud)

Location - Remote

Duration - 12+ Months

Experience - 15 18+ years overall IT experience with recent 5 years in AI/ML architecture in Health-IT

Job Summary: We are seeking an experienced AI/ML Architect to design, build, and scale enterprise-grade AI/ML solutions for the healthcare domain on AWS Cloud. The ideal candidate will bridge business, clinical, and technology teams to deliver secure, compliant, and scalable ML platforms supporting use cases such as clinical decision support, population health, claims analytics, fraud/waste/abuse detection, medical imaging, NLP on clinical text, and patient engagement.

Key Responsibilities

• AI/ML Architecture & Strategy
• Define end-to-end AI/ML architecture for healthcare solutions on AWS, covering data ingestion, feature engineering, model training, deployment, monitoring, and governance.
• Design scalable, reusable ML platforms aligned with enterprise architecture and healthcare compliance standards (FHIR, HL7, HIPAA, HITRUST).
• Evaluate and recommend ML frameworks, AWS AI services, and MLOps tools based on business needs.
• Healthcare Use Case Enablement
• Partner with clinical, payer, and business stakeholders to translate healthcare problems into ML-driven solutions.

Architect solutions for:

• Clinical analytics & predictive modeling
• NLP on EHR/EMR data (FHIR, HL7) & integrations
• Medical imaging and diagnostics
• Ensure explainability, bias mitigation, and model interpretability for clinical and regulatory acceptance.
• AWS Cloud & MLOps
• Architect ML solutions using AWS services such as:
• Amazon SageMaker (training, pipelines, feature store, model registry)
• S3, Glue, Athena, Redshift
• Lambda, Step Functions, ECS/EKS
• API Gateway, CloudWatch, IAM
• Design and implement MLOps pipelines (CI/CD for ML) including automated training, validation, deployment, monitoring, and retraining.
• Implement model performance monitoring, drift detection, and auditability.
• Data Engineering & Governance
• Collaborate with data engineering teams to design secure, scalable data pipelines for structured and unstructured healthcare data.
• Support data standards such as FHIR, HL7, ICD, CPT, SNOMED.

Required Skills & Qualifications

• Core Technical Skills
• Strong expertise in Machine Learning, Deep Learning, and AI architecture
• Proficiency in Python, PySpark, and ML libraries (TensorFlow, PyTorch, Scikit-learn)
• Hands-on experience with AWS AI/ML services, especially Amazon SageMaker
• Solid understanding of MLOps, CI/CD, and model lifecycle management
• Experience with big data technologies (Spark, data lakes, feature stores)
• Healthcare Domain Expertise
• Strong understanding of Health-IT data ecosystems (EHR/EMR, clinical data)
• Knowledge of healthcare regulations: HIPAA, HITRUST, PHI handling
• Experience with healthcare interoperability standards (FHIR, HL7)
• Preferred Qualifications
• AWS Certifications (e.g., AWS Certified Machine Learning Specialty, Solutions Architect)

Soft Skills

• Strong stakeholder communication and storytelling skills
• Ability to translate complex ML concepts into business and clinical language
• Strategic thinker with hands-on execution mindset
• Experience working in regulated, enterprise environments