Careers

Key Responsibilities

Generative AI & LLM Implementation:

• Design, build, and deploy Generative AI solutions using Google Vertex AI, Gemini APIs, and related GCP services.
• Identify and prioritize high-value use cases (e.g., claims summarization, member communications, eligibility Q&A, document processing, knowledge retrieval).
• Implement prompt engineering, RAG (retrieval-augmented generation), and fine-tuning strategies to ensure accuracy in regulated environments.
• Ensure solutions align with responsible AI principles, including explainability, auditability, and HIPAA compliance.
• Incorporate secure prompt handling, data redaction, and model access controls to prevent data leakage and unauthorized use.

ML Pipeline Development, Infrastructure & Security:

• Build and maintain scalable, end-to-end ML pipelines (data ingestion → deployment → monitoring).
• Leverage GCP tools such as Vertex AI Pipelines, BigQuery ML, Dataflow, and Cloud Composer.
• Establish MLOps practices (CI/CD, model versioning, monitoring, automated retraining).
• Implement security-by-design principles across ML pipelines, including:
  • Data encryption (at rest and in transit)
  • Identity and Access Management (IAM) with least-privilege access
  • Secure API design and service authentication
  • Logging, monitoring, and audit trails for all ML systems
  • Align infrastructure and workflows with SOC 1 / SOC 2 controls (security, availability, confidentiality) and CMMC practices where applicable
  • Partner with security and compliance teams to support audits, evidence collection, and control validation

Business Stakeholder Collaboration:

• Partner with finance, operations, and leadership to identify AI opportunities that reduce administrative burden and improve outcomes.
• Translate business problems into well-defined AI/ML solutions.
• Communicate technical concepts clearly to non-technical stakeholders.
• Serve as an internal advocate for AI/ML adoption, secure development practices, and responsible AI governance.