End Date 18 Sep 2026
Basic Section
Required Experience 8 - 14 years
Organisational
Country IN
State KARNATAKA
City BENGALURU
Skills
Skill
Solution Architecture Enterprise Architecture Cloud Computing ITIL SOA IT Strategy IT Service Management Project Management Service Delivery SDLC Oracle Management Consulting Android SAP Implementation ABAP Incident Management MySQL
Education Qualification
CERTIFICATION
Job Description
About Us
Omega Healthcare Management Services® (Omega Healthcare) is an AI-driven healthcare solutions company that partners across the healthcare ecosystem to deliver breakthrough results by reimagining and elevating revenue operations.
Powered by the Omega Digital Platform®, our agentic AI engine leverages adaptive intelligence to drive automation, complemented by deep human expertise to help optimize performance and deliver sustained financial and clinical outcomes -while enhancing patient satisfaction.
With a strong global presence spanning four countries, our team of 30,000+ dedicated professionals collaborate with hospitals, physician groups, and healthcare providers to streamline operations, improve financial performance and elevate patient care outcomes.
We combine our deep domain expertise, advanced technology, and a customer-centric approach in delivering innovative and scalable healthcare support solutions.
Position Title: Solution Architect - AI/ML
Department: Engineering & Technology | Function: AI/ML Platform Engineering
Role Summary
The Data Science Solution Architect is a senior technical leadership role responsible for defining the enterprise architecture, scaling strategies, and production infrastructure for domain-specific SLMs, multi-agent systems, and advanced retrieval architectures (including RAG and GraphRAG). This role bridges the gap between cutting‑edge AI research and enterprise‑grade deployment. The Architect owns the design of the foundational "agent harness" - setting the standards for secure tool execution, memory management, and guardrails - while ensuring models and retrieval pipelines are optimized for latency, cost, and accuracy across distributed hardware.
Key Responsibilities
- Architecture & Strategy: Define the enterprise architecture, standards, and roadmap for AI systems. Design models and retrieval pipelines optimized for accuracy, latency, and cost across diverse hardware envelopes (e.g., single-GPU, A100/H100 inference, and training envelopes). Establish the enterprise ontology and knowledge representation strategy.
- GraphRAG & Advanced Retrieval Architecture: Lead the architectural design of hybrid retrieval systems. Scale Vector and Graph Databases (e.g., Neo4j) to handle massive enterprise corpora. Design systems that seamlessly route between standard semantic search and GraphRAG (community detection, multi‑hop reasoning) based on query complexity.
- Agent Harness Engineering & Orchestration: Architect the enterprise standard for multi‑agent workflows (e.g., LangGraph, AutoGen). Design the core "harness": robust state management, distributed memory architectures, secure tool‑execution sandboxes, and deterministic guardrails (input/output validation, PII redaction, hallucination checks) that make autonomous agents safe for production.
- Inference, Serving & Optimization: Own inference architecture using modern serving engines (vLLM, SGLang). Drive latency/throughput optimization via continuous batching, quantization, speculative decoding, FlashAttention, and optimized runtimes (TensorRT / TensorRT-LLM).
- Distributed Training & Scaling: Lead multi‑GPU training architectures (DDP / FSDP), implementing data- and model‑parallel strategies, gradient accumulation, and Mixture‑of‑Experts (MoE) scaling efficiency for specialized SLMs.
- Cloud, MLOps & Production Readiness: Architect secure AI solutions on AWS or Azure. Define enterprise MLOps standards for CI/CD, adapter versioning, checkpointing, and CI/CD for agentic pipelines and knowledge graph updates.
- Leadership & Mentorship: Act as the technical authority and design‑review leader. Mentor Data Scientists and ML Engineers, fostering a research‑driven culture that rapidly operationalizes the latest AI advancements.
Required Skills & Expertise
- Enterprise AI Architecture: Cloud‑based ML architecture (AWS/Azure), MLOps, enterprise security, and AI governance.
- Advanced Retrieval & GraphRAG: Deep architectural knowledge of Knowledge Graphs (Neo4j, Amazon Neptune), Vector Databases at scale, ontology design, and Microsoft GraphRAG or custom map‑reduce graph reasoning patterns.
- Agentic Frameworks & Harnessing: Multi‑agent orchestration (LangGraph, AutoGen, CrewAI), enterprise harness engineering, secure tool routing, state persistence, and Guardrails AI/NeMo Guardrails.
- Inference & Runtimes: Deep expertise in vLLM, SGLang, TensorRT, TensorRT-LLM, and hardware‑aware tuning.
- Distributed Systems & Model Internals: Multi‑GPU training (DDP/FSDP), Mixture‑of‑Experts (MoE), sparse/long‑context attention, and FlashAttention.
- Advanced Research Operationalization: Strong track record of adapting complex research concepts into scalable enterprise pipelines.
Preferred / Nice to Have
- Domain Knowledge: Strong preference for experience within the Healthcare and Medical domain, specifically with Revenue Cycle Management (RCM).
- Deep knowledge of speculative decoding, structured/guided decoding, and KV‑cache optimization.
- Responsible AI frameworks, model risk management, and regulatory compliance.
- Experience defining AI strategy at an organizational or platform level.
Educational Qualification
- ME / MTech / MCA / PhD in Computer Science, AI, or a related field.
- PhD highly preferred for advanced architectural leadership and research translation.
Experience
- 10+ years in Data Science, Machine Learning, and Enterprise Architecture.
- Extensive proven experience architecting, scaling, and deploying production‑grade AI/ML, LLM/SLM, and complex retrieval systems at the enterprise level.
Designation Solution Architect - Technology
Closing Date 18 Sep 2026
Grade T3B