Responsibilities for Internal Candidates
- Design end‑to‑end AI / GenAI solution architectures covering data ingestion, feature engineering, model development, deployment, and monitoring.
- Lead solutioning for healthcare use cases such as:
- Claims processing & Payment Integrity
- Care Management & Population Health
- Clinical document processing and summarization
- Provider & member analytics
- Architect agentic AI and RAG‑based solutions using LLMs for unstructured healthcare data (clinical notes, policies, contracts, medical records).
- Translate business problems into AI‑driven architectures, ensuring alignment with ROI, scalability, and regulatory requirements.
- Define reference architectures, NFRs, and technology standards for AI platforms.
- Collaborate with data science teams on:
- Model selection and evaluation
- Prompt engineering and orchestration strategies
- Ensure solutions comply with HIPAA, PHI/PII, security, governance, and AI risk frameworks.
- Provide technical leadership during pre‑sales, client workshops, proposals, and solution walkthroughs.
- Mentor engineers and junior architects; review designs and ensure architectural best practices.
- Stay current with emerging trends in GenAI, agentic workflows, healthcare AI regulations, and cloud AI services.
Qualifications
Core Technical Skills
- Strong foundation in AI/ML, Deep Learning, and GenAI architectures
- Hands‑on experience with LLMs, RAG pipelines, vector databases, and AI agents
- Experience with unstructured data processing (NLP, OCR, Intelligent Document Processing)
- Solid understanding of data engineering & analytics stacks
- (Data lakes, data warehouses, ETL/ELT pipelines)
- Proficiency with cloud platforms (AWS / Azure / GCP) and AI services
- Experience with microservices, APIs, containerization (Docker/Kubernetes)
- Knowledge of payer and provider workflows
- Exposure to regulatory and compliance requirements in healthcare
- Proven experience delivering AI solutions in healthcare operations or clinical workflows
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