DataArt
Principal AI Solutions Architect (Healthcare & Life Science)
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Position overview
As a Principal AI Solutions Architect within DataArt’s Healthcare & Life Sciences practice, you will bridge the gap between advanced AI tools and real-world business impact. You’ll operate at the intersection of hands-on prototyping, cross-functional collaboration, and stakeholder education. Your focus: drive measurable client outcomes through AI-enabled efficiencies and new digital experiences.
You will shape how AI is adopted across the healthcare and life sciences industries — from intelligent copilots and personalization engines to developer acceleration and operational optimization — by prototyping real solutions, articulating business value, and driving adoption from C-suite to delivery teams.
You will work closely with client account teams (50+ active clients) and DataArt’s AI + Data Lab to identify high-value use cases, prototype rapidly, validate with stakeholders, and showcase scalable solutions that clients can adopt.
Responsibilities
- Lead ideation and scoping of AI/ML proof of concepts (PoCs) in coordination with client account leaders
- Rapidly prototype and iterate AI features (chatbots, NLP services, recommender systems, etc.)
- Direct internal DataArt teams to execute PoCs with clarity, velocity, and alignment
- Own the "show-and-tell" lifecycle: internal demo → stakeholder validation → external client presentation
- Develop GenAI applications based on RAG variations, Agentic, hybrid models. Leverage Agentic SDLC and advocate for adoption. Develop with hyperscaler AI tools (Azure, GCP, AWS), LLM APIs, vector search, GenAI, Agentic SDKs and Agentic Runtimes (Agent Core, Agent Engine, etc.)
- Define reusable AI building blocks and Healthcare-specific agent scaffolds that feed into the Connect AI ecosystem
- Build working solutions integrated with cloud data platforms like Snowflake and Databricks
- Embed AI into client-facing products or operations (e.g., personalization, crew ops, service automation)
- Shape internal best practices for GenAI use in SDLC and Healthcare product lifecycle — from opportunity discovery to ops telemetry
- Create AI demo assets: notebooks, dashboards, blog content, or walkthrough videos
- Run workshops and working sessions with client teams and business leaders
- Act as a translator and evangelist, helping stakeholders understand responsible AI adoption and potential ROI
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Requirements
- Hands-on experience with AI services from at least one major cloud provider (Azure, AWS, GCP)
- Demonstrated ability to operate at Director level or above in client engagements, influencing both technical and business stakeholders
- Experience with designing and implementing RAG based applications
- Strong background with Snowflake and/or Databricks for data engineering and AI pipelines
- Proven success leading client-facing PoCs or MVPs from inception to delivery
- Prior exposure to Healthcare or Life Sciences sector
- Strong understanding of Healthcare and/or Life Sciences workflows, regulatory frameworks (HIPAA, CMS-0057-F, GDPR, ISO 13485, ISO 42001, etc.), and industry challenges.
- Ability to translate fuzzy business goals into AI-enabled outcomes, structuring initiatives from ambiguous needs into scoped, testable, and scalable components
- Fluency in evaluating tradeoffs between open-source LLMs and commercial APIs, and designing cost-efficient, scalable architectures
- Strong communicator comfortable with both technical teams and business stakeholders
- Ability to travel to client site (US, EU)
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Nice to have
- Exposure to healthcare data standards (FHIR, HL7) and integrations (eRx, Clearinghouses, TEFCA).
- Knowledge of GenAI fine-tuning, vector DBs, or orchestration frameworks (LangChain, Semantic Kernel, etc.)
- Experience developing internal enablement materials or technical content
- Awareness of responsible AI practices
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