Principal AI Solutions Architect Healthcare

TechVerseCorp

Key Responsibilities

Solution Analysis & Architecture

• Lead the technical discovery and analysis phase for healthcare AI use cases.

• Translate clinical and business requirements into end-to-end AI solution architectures.

• Define system architecture covering:

  • Data ingestion & pipelines

  • AI/ML model lifecycle

  • Application & integration layers

  • Deployment, monitoring, and scalability

• Ensure solutions meet clinical safety, data privacy, and regulatory expectations.

AI & Data Architecture Leadership

• Design architectures for:

• Machine Learning & Deep Learning models

• Generative AI (LLMs, clinical assistants, decision support)

• Medical imaging & diagnostics pipelines

• Define data engineering architectures including:

• Data lakes, feature stores, and analytics platforms

• Structured & unstructured healthcare data handling

• Establish MLOps standards for:

• Model training, validation, deployment, and monitoring

• Versioning, retraining, and drift detection

Technical Leadership & Delivery Ownership

Own the technical delivery of AI healthcare solutions end to end.

Lead and mentor a local engineering team ( 5 engineers).

Act as the technical authority across:

Design reviews

Architecture decisions

Code and model quality

Work closely with Product, CTO, and stakeholders to ensure alignment between vision and execution.

Security, Compliance & Standards

• Ensure architectures comply with healthcare data protection principles (HIPAA-like controls, data governance, auditability).

• Define security-by-design practices including:

• Data encryption

• Access control

• Secure model deployment

• Establish architectural standards and best practices for healthcare AI systems.

Collaboration & Stakeholder Engagement

• Act as the technical bridge between business, product, and engineering teams.

• Participate in solution discussions, requirement refinement, and technical decision-making.

• Support internal product strategy with technical insights and feasibility assessments.

Required Technical Skills & Expertise

• AI & Machine Learning

• Strong experience in:

• Machine Learning & Deep Learning architectures

• Model lifecycle management in production

• Experience with:

• Clinical decision support systems

• Medical imaging or diagnostics AI (preferred)

• Generative AI

• Hands-on experience designing or integrating:

• LLM-based systems

• AI assistants or decision-support tools

• Understanding of prompt engineering, orchestration, and model governance.

• Data Engineering & Analytics

• Strong knowledge of:

• Data pipelines, ETL/ELT processes

• Healthcare data (structured & unstructured)

• Experience with analytics platforms and large-scale data processing.

• MLOps & Cloud Architecture

• Experience designing and implementing:

• CI/CD pipelines for ML

• Model monitoring, retraining, and performance tracking

• Cloud-native architecture experience (AWS, Azure, GCP, or OCI).

Leadership & Professional Skills

• Proven ability to own architecture and technical delivery.

• Strong analytical and problem-solving mindset.

• Ability to translate complex business and clinical needs into technical solutions.

• Excellent communication skills with technical and non-technical stakeholders.

• Comfortable setting standards, making decisions, and being accountable.