Director - Healthcare & Generative AI Solutions Lead
Location: India (preferred: Ahmedabad or major metro)
Department: Strategy / Sales & Marketing / Innovation
Reports to: CEO (with close alignment to Sales, Delivery & Engineering/AI teams)
About the Role
We are looking for an executive-level leader based in India who deeply understands the US healthcare market, the IT services industry, and how to use Generative AI to solve real business problems.
Your primary mission is to:
1. Analyze the US healthcare market and identify high-value problems within healthcare customer segments (mid/large providers, payers, MedTech/HealthTech, etc.).
2. Design and build AI-powered solution accelerators (LLM-based tools, workflows, reference architectures, PoCs) that our sales team can sell and our delivery team can implement.
This role sits at the intersection of market research, healthcare domain, generative AI, solutioning, and GTM.
Key Responsibilities
1. Market & Customer Research (US Healthcare Focus)
· Conduct deep research on the US healthcare ecosystem (providers, payers, MedTech, HealthTech) with a lens on where AI can create value (efficiency, automation, quality, compliance, patient/clinician experience).
· Analyze trends in healthcare + AI: automation, documentation, coding, RCM, care coordination, analytics, patient engagement, etc.
· Map and prioritize opportunities across mid-level, enterprise, and enterprise high based on revenue potential, feasibility, and alignment with our capabilities.
· Run structured discovery with customers/prospects and internal stakeholders to validate pain points and AI use cases.
2. Generative AI & Accelerator Development
· Identify concrete generative AI use cases in healthcare—for example:
o Clinical / operational documentation assistance
o Coding & RCM support (summarization, coding suggestions, denial analysis)
o Patient communication & education content
o Knowledge retrieval and summarization for clinicians or operations teams
o Workflow automation for care coordination, scheduling, intake, etc.
· Work with technical/AI teams to design and build accelerators, such as:
o LLM-based assistants, copilots, or workflow bots
o RAG (Retrieval-Augmented Generation) solutions on client data
o Templates, frameworks, reference architectures, and reusable components
· Define the architecture at a high level (data sources, security, APIs, model providers, integration points) and ensure alignment with compliance standards (especially HIPAA/PHI handling).
· Ensure accelerators are repeatable, configurable, and implementable across multiple clients, not one-off projects.
3. Go-to-Market & Sales Enablement
· Package AI accelerators into clear offerings:
o Problem statement, solution overview, value proposition
o Target ICPs and personas (CIO, CDO, CMO, COO, VP Rev Cycle, etc.)
o Pricing and commercialization approach (fixed-fee PoCs, pilots, bundles, etc.)
· Create sales and marketing assets with a strong AI story: decks, one-pagers, demo scripts, ROI/value calculators.
· Train sales, pre-sales, and account managers on how to position our AI offerings and answer common objections (safety, accuracy, compliance, ROI).
· Support strategic pursuits where our AI accelerators can be a differentiator.
4. Cross-Functional Collaboration
· Work closely with Delivery, AI/Engineering, Sales, and Marketing to ensure solutions are technically feasible and commercially viable.
· Act as the bridge between business and AI teams: translate business problems into AI solution requirements and vice versa.
· Provide “voice of the market” feedback into our AI roadmap and service lines.
5. Metrics & Continuous Improvement
· Define and track KPIs for AI accelerators:
o Pipeline generated, deals influenced, revenue and margins
o Impact metrics (time saved, error reduction, cycle time, etc.)
· Run pilots, gather feedback, and iterate on the offerings.
· Maintain a prioritized AI accelerator roadmap aligned with our 3–5 year healthcare growth goals.
Required Experience & Qualifications
· 10–15 years of experience in IT services / IT consulting, with:
o Minimum 5–7 years focused on the US healthcare market (providers, payers, MedTech/HealthTech).
· Hands-on experience with Generative AI in a business / product / solution context is essential, for example:
o Defining and delivering LLM-based use cases (ChatGPT-like tools, copilots, AI assistants, or RAG solutions).
o Working with AI/ML engineers, data teams, or cloud AI platforms (e.g., Azure, AWS, GCP, OpenAI/other LLM providers).
o Understanding strengths/limitations of LLMs, prompt design, evaluation, guardrails, and enterprise adoption challenges.
· Strong background in at least one of:
o Healthcare digital transformation, clinical workflows, RCM, patient engagement, care management, or healthcare analytics.
· Proven track record building or productizing solutions/accelerators in an IT services environment (not just custom one-off projects).
· Comfortable interacting with CXO/VP-level stakeholders in US healthcare organizations and explaining AI concepts in simple business language.
· Understanding of US healthcare regulations and standards (HIPAA, PHI handling; knowledge of FHIR/HL7, interoperability, value-based care, etc. is a strong plus).
· Experience working with distributed/global teams with some overlap to US time zones.
· Excellent English communication – written, verbal, and presentation.
· MBA, MHA, MPH, or a related advanced degree is a plus but not mandatory if experience is very strong.
Key Skills & Competencies
· Strategic & product thinking – can connect healthcare pain points to AI-powered offerings and revenue.
· Generative AI literacy – not necessarily a coder, but deeply comfortable with LLM concepts, use cases, and limitations.
· Healthcare domain depth – understands real-world provider/payer/MedTech challenges.
· Commercial mindset – thinks in terms of ICPs, pipeline, deal sizes, margins, and repeatability.
· Execution & ownership – can take a concept from idea → PoC → accelerator → offering.
· Stakeholder & client management – credible with both business leaders and technical teams.
· Entrepreneurial – comfortable with ambiguity, building from scratch, and iterating quickly.
