Job Description

We are seeking a technically deep, hands-on AI Architect / Forward Deployed Engineer (AVP) to embed directly within client environments and own the end-to-end technical delivery of EXL's Agentic AI and Generative AI solutions for large U.S. healthcare payers. This role sits at the intersection of cloud architecture, AI agent engineering, and healthcare payer domain expertise—spanning Prior Authorization, Utilization Management, Care Management, and Claims.

As a Forward Deployed Engineer, you will sit alongside client teams, understand their real workflows and constraints firsthand, and rapidly architect, build, deploy, and harden AI solutions inside their infrastructure. You will move fluidly between whiteboard architecture and hands-on implementation—writing code, configuring environments, integrating with legacy systems, and iterating in tight feedback loops until solutions are live and creating measurable value.

The ideal candidate brings a rare combination of hands-on AI/ML and cloud architecture depth, agentic AI engineering experience, and deep understanding of U.S. healthcare payer operations. Deep cloud, AI Agents, and healthcare experience are mandatory for this role.

1. AI Solution & Reference Architecture Ownership

• Own the end-to-end technical architecture for AI-powered healthcare solutions—covering agent orchestration, LLM serving, retrieval pipelines, data ingestion, and integration with legacy payer systems.
• Define and maintain reusable reference architectures, design patterns, and architectural guardrails for Agentic AI solutions that can be rapidly adapted across client deployments.
• Establish and govern AI Dev/QA/Prod environments, CI/CD pipelines, and MLOps/LLMOps practices, doubling as the AIOps architect for the team.

1. Forward Deployment & Hands-On Delivery

• Embed within client environments to understand real workflows, data, constraints, and edge cases firsthand translating them directly into working AI solutions.
• Personally build, configure, and deploy solutions inside client infrastructure ("bring your own subscription" / client-hosted), not just design them.
• Operate in fast, iterative feedback loops—prototype, demo, gather input, and harden to production at speed.
• Troubleshoot live deployments, resolve integration and performance issues, and ensure solutions run reliably in the client's operational context.
• Capture reusable learnings, accelerators, and IP from each deployment to shorten time-to-value on the next.

1. Cloud Architecture & Engineering

• Architect and implement secure, scalable, multi-cloud and hybrid deployments across Azure, AWS, and GCP—including within client-controlled subscriptions and networks.
• Build infrastructure-as-code, containerized (Docker/Kubernetes) deployments, GPU capacity planning, and cost/latency optimization for LLM workloads.
• Implement AI gateway design, PHI de-identification layers, and enterprise integration patterns with payer platforms and data stores.
• Ensure deployments meet stress/throughput targets (e.g., high-volume FHIR record processing) with resilient, observable systems.

1. AI Agents & Generative AI Design

• Architect and build multi-agent systems—policy decision-tree agents, clinical review agents, data-gathering agents, and IDP agents—that replicate expert human judgment with human-in-the-loop governance.
• Set standards for prompt engineering, RAG pipelines, fine-tuning/domain adaptation, embeddings, and vector search.
• Build agent execution layers for legacy system navigation (mainframe, Onbase/ECM, CareRadius, Salesforce, Epic) using MCP servers and browser/desktop automation.
• Champion explainability-first, audit-ready AI design suitable for CMS audits and payer compliance reviews.

1. Healthcare Domain Integration

• Apply deep knowledge of payer workflows—Prior Authorization, Utilization Management, Care Management, Claims, and Provider operations—to translate operational challenges into deployed AI solutions.
• Integrate AI solutions with clinical criteria sources (MCG, InterQual, CMS medical policies) and payer platforms (QNXT, Facets, CareRadius) via API-first, standards-based design (HL7, FHIR, X12 EDI).
• Support alignment with regulatory drivers such as CMS-0057-F interoperability/prior-auth mandates.

1. Governance, Security & Compliance

• Champion privacy-first design, data anonymization, and compliance with HIPAA and PHI/PII handling requirements.
• Establish governance-grade AI controls—model monitoring, bias detection, drift management, and human-in-the-loop overrides—within each deployed environment.

Bachelor’s degree in computer science, Engineering, Data Science, or a related technical field. Master's degree (M.Tech / MS / MBA) preferred.

Experience

• 10+ years of progressive experience in software/AI engineering, cloud architecture, or forward deployed/solution engineering roles.
• Deep cloud experience is a must — proven hands-on architecture and deployment across at least one major hyperscaler (Azure / AWS / GCP), including production-grade, secure, scalable systems in client environments.
• Deep AI Agents experience is a must — demonstrated design and delivery of Agentic AI / multi-agent and Generative AI systems (LLMs, RAG, orchestration frameworks) in production.
• Deep healthcare experience is a must — strong understanding of U.S. healthcare payer operations (Prior Auth, UM, Care Management, or Claims).
• Proven track record of personally taking AI solutions from POC to live production—hands-on, not purely advisory.

Technical Skills

• Hands-on and architectural expertise in LLMs, embeddings, vector search, prompt engineering, and RAG pipelines.
• Strong coding ability (Python and related AI/ML stacks) with the ability to ship production-quality solutions directly.
• Proficiency with cloud AI platforms: Azure OpenAI, AWS Bedrock (Claude/Sonnet), GCP Vertex AI.
• Expertise with agent orchestration frameworks: LangChain, LangGraph, CrewAI, AutoGen, or equivalent.
• Strong understanding of MCP (Model Context Protocol), A2A protocols, and multi-agent system design.
• Infrastructure-as-code, Docker/Kubernetes, CI/CD, MLOps/LLMOps, and GPU capacity planning.
• Secure API design, OAuth2/JWT, enterprise integration patterns, and healthcare data standards (HL7, FHIR, X12 EDI 837/835/270/271/276/277).

Domain Skills

• Deep understanding of payer clinical and claims workflows and integration with platforms such as QNXT, Facets, CareRadius.
• Familiarity with clinical criteria sources (MCG, InterQual) and CMS regulatory frameworks (including CMS-0057-F).

Working Style

• Comfortable being embedded on-site/in-client-environments with significant travel and direct, day-to-day collaboration with client technical teams.
• Bias for action, strong ownership, and ability to operate autonomously in ambiguous, fast-changing conditions.
• Ability to lead distributed US + offshore engineering support while remaining hands-on.

Preferred Qualifications

• Experience building AI Centers of Excellence or reusable agent frameworks/accelerators for healthcare.
• Patent holder, published researcher, or recognized contributor in AI/healthcare innovation.
• Experience with Databricks, Snowflake, or similar data platforms in a healthcare context.
• Cloud/AI certifications (Azure Solutions Architect, AWS Solutions Architect Professional, GCP Professional Architect, or equivalent).