Role Summary

Design and deliver enterprise-grade AI solutions for regulated healthcare environments. Own architecture for assigned products and workstreams across Agentic AI, GenAI, traditional ML, application, and data layers. Stay hands-on through incubation, contribute to pre-sales and customer engagements alongside Lead Architects and Delivery Leaders, and mentor engineers on AI engineering practices. Key Responsibilities:

End-to-end AI architecture: Architect Agentic AI, GenAI, RAG, and traditional ML solutions across assigned products and workstreams, from design through production deployment.

Agentic systems: Design and build multi-agent flows — agent orchestration, tool/function calling, planning and reasoning patterns, memory, guardrails, and human-in-the-loop oversight.

Hybrid AI design: Combine deterministic rules, statistical models, explainable ML, and LLM-based reasoning for high-precision, auditable outcomes.

GenAI stack: Design LLM orchestration, RAG, vector stores, prompt engineering, evaluation, grounding, and HITL workflows.

Governance & compliance: Apply AI governance, security, and compliance practices — explainability, PHI handling, HIPAA, HITRUST, and responsible AI.

MLOps / LLMOps: Implement model and agent CI/CD, evaluation pipelines, observability, and cost/latency optimization.

Pre-sales: Contribute to solutioning, estimation, execution approach, and delivery plans; co-author technical proposals, RFP/RFI responses, and SoWs under Lead Architect guidance.

Customer partnership: Run discovery sessions, requirements workshops, and solution walkthroughs with client architects and business stakeholders; partner with Lead Architects and Delivery Leaders to understand customer context.

Project incubation: Hands-on build of PoCs, architecture spikes, and MVPs — establishing the technical foundation that delivery teams scale.

Application & data architecture: Design microservices, APIs, event-driven patterns, data lakes/lakehouses,



streaming pipelines, and EMR/enterprise integrations for AI products.

Business outcomes: Translate problems into AI capabilities across healthcare sub-segments like EMR, Patient services and engagement, Medical devices, Medical Imaging, RCM, etc.

Mentorship: Mentor engineers and junior architects; run design reviews within the workstream.

Required

Skills

Agentic AI

Hands-on experience building agentic systems — single-agent and multi-agent flows with planning, tool/function calling, memory, and guardrails.

Working experience with at least one of LangGraph, AutoGen, CrewAI, Bedrock Agents, or OpenAI Agents/Assistants.

Familiarity with evaluation, observability, tracing, and safety controls for agentic workloads.

GenAI, ML & AI Governance

Solid working experience across the GenAI stack: LLM orchestration, RAG pipelines, vector databases, prompt engineering, evaluation, grounding, and HITL workflows.

Hands-on with at least one major platform — AWS Bedrock, Azure OpenAI, OpenAI, Anthropic, or an open-source LLM ecosystem.

Experience building hybrid AI flows that combine rules, statistical models, ML, and LLM reasoning.

Working knowledge of AI governance, security, and compliance: PHI handling, HIPAA, explainability, responsible AI.

Healthcare Domain

Hands-on experience building healthcare AI solutions in at least one of: medical imaging, medical devices, EMR, patient engagement, RCM, patient services, life sciences (clinical trials, genomics).

Working knowledge of FHIR, HL7, EDI 837/835, or EHR integrations.





Ability to deliver AI capabilities for areas such as denial prediction, revenue integrity, coding optimization, risk analytics, workflow automation, or copilots/assistants.

Pre-sales & Solutioning

Experience contributing to pre-sales — solutioning, estimation (effort, team mix, timeline), execution method, and delivery plans under Lead Architect guidance.

Experience co-authoring technical proposals, RFP/RFI responses, and SoWs; comfortable presenting solution components in client conversations.

Effective collaboration with other Architects, Delivery Leaders, and Sales to understand customer context.

Nice to Have

Open-source contributions, technical blogs, patents, or publications in AI/ML.

Exposure to healthcare interoperability platforms, payer-provider data exchange, or clinical decision support.

Certifications in cloud architecture, AI/ML, or healthcare compliance.

Qualifications

Bachelor's or Master's in Computer Science, Engineering, Data Science, or related field.

8–12 years of total IT experience.

4–6+ years of dedicated technical architecture experience across AI/ML and application/data architecture.

Track record of delivering production AI in regulated environments (healthcare preferred).

About CitiusTech: CitiusTech is a global technology services, consulting, and business solutions enterprise 100% focused on the healthcare and life sciences industry. We enable 140+ enterprises to build a human-first ecosystem that is productive, effective, and equitable. Leveraging deep domain expertise and next-generation technologies including AI, Cloud, Data, and Intelligent Automation, we assist our clients to realize their vision, accelerate transformation, and achieve business outcomes.

With 7,700+ healthcare technology professionals worldwide, CitiusTech powers digital innovation, business transformation, and industry-wide convergence through next-generation technologies, solutions, and products.

📌 AI Solutions Architect (Maharashtra)
🏢 CitiusTech
📍 Maharashtra