AI Architect – Healthcare (GenAI & Data Platforms)

About the role

Virtelligfence is looking for a senior AI Architect with deep healthcare expertise to lead the design and deployment of enterprise AI solutions across payer, provider and value‑based care environments. The role bridges AI, data engineering and clinical operations to drive innovation, improve outcomes and optimise financial performance in highly regulated settings.

Key responsibilities

• Define and execute an enterprise AI strategy aligned with clinical quality, revenue‑cycle and population‑health priorities.
• Architect AI solutions for clinical decision support, documentation automation, care pathways, coding, billing optimisation, denial prediction and prior‑authorization.
• Design and deliver GenAI and LLM‑based tools such as clinical copilots, RAG pipelines and patient‑engagement assistants.
• Build agentic AI systems that automate workflows across EHR, claims and enterprise platforms.
• Create modern healthcare data ecosystems integrating Epic, Cerner, HL7/FHIR, Snowflake, Databricks and lake‑house architectures.
• Establish MLOps/LLMOps frameworks for model lifecycle management, monitoring, validation and governance.
• Ensure HIPAA compliance and responsible AI practices throughout solution delivery.
• Collaborate with clinical, operational and executive stakeholders to translate business needs into scalable AI products.
• Lead architecture reviews, mentor technical teams and support pre‑sales engagements.

Required profile

• Bachelor’s degree in Computer Science, Data Science, Engineering or related field.
• 10–15+ years of experience in technology, data or solution architecture.
• 5+ years designing AI/ML solutions in healthcare or other regulated industries.
• Proven track record delivering production‑grade AI systems in complex healthcare environments.
• Strong communication skills with the ability to engage clinical and executive audiences.

Required skills

• Python, SQL
• PyTorch, TensorFlow, Scikit‑learn
• LLM platforms (OpenAI, Claude, Llama)
• RAG architectures, vector databases, agent‑based systems
• Distributed data pipelines and real‑time processing frameworks
• Cloud platforms: Azure, AWS, GCP
• Modern data stacks: Databricks, Snowflake, Spark
• Healthcare data models: Epic (Cogito, Clarity, Caboodle), Cerner, HL7, FHIR