Senior AI/ML Engineer (5+ Years Experience | Bangalore Onsite | Upto 45LPA)

About the Role Our client is seeking a Senior AI/ML Engineer to build and operate production-grade Large Language Model (LLM) systems, knowledge graphs, and embedding-based retrieval pipelines for healthcare revenue cycle management. The ideal candidate will have hands-on experience deploying self-hosted LLMs, designing embedding and graph-based retrieval, and building evaluation and monitoring pipelines. This role offers full ownership of applied LLM infrastructure in a regulated healthcare environment, solving complex domain-specific problems spanning coding, claim edits, denials triage, appeal generation, and payer-rule reasoning. Key Responsibilities Deploy, fine-tune, and operate self-hosted LLMs such as Llama, Qwen, MedGemma, using vLLM, SGLang, TensorRT-LLM.Own fine-tuning workflows (SFT, LoRA, QLoRA, DPO) on clinical notes, claims, and payer-rule data.Optimize GPU usage, latency, batching, and cost for production LLM inference.Design, maintain, and leverage knowledge graphs encoding ICD-10-CM, CPT, HCPCS, modifiers, HCC, NCCI edits, LCD/NCD policies, and payer rules.Build embedding-based retrieval pipelines over clinical notes, claims, denial reasons, and payer-policy corpora.Combine graph traversal and dense retrieval to ensure auditable, grounded outputs.Maintain ingestion, versioning, and quality of underlying knowledge sources (CMS, AHA, AMA, NCCI, payer bulletins).Build continuous evaluation pipelines with offline benchmarks and online monitoring for drift, regressions, hallucinations, and output quality.Track business metrics: coding accuracy, denial rate impact, clean-claim rate, cost per chart, end-to-end latency.Design LLM prompts, context pipelines, structured outputs (JSON, function calls, constrained decoding).Implement Retrieval-Augmented Generation (RAG) pipelines over medical coding standards and payer policies.Build MCP servers and multi-step agentic workflows with audit trails and human-in-the-loop checkpoints.Define deterministic vs. LLM-based tool boundaries for reliable AI-assisted workflows. Required Skills 5+ years of ML/AI engineering experience.6+ months of production experience with LLM systems.Hands-on deployment of self-hosted LLMs (vLLM, SGLang, TensorRT-LLM, or equivalent).Expertise in embedding-based retrieval and/or knowledge graph design.Experience owning evaluation infrastructure: offline benchmarks, online monitoring, drift/regression detection.Strong Python, PyTorch, and Hugging Face experience.Production experience in monitoring, incident response, and system ownership. Nice-to-Have Skills Fine-tuning workflows: SFT, LoRA, QLoRA, DPO on domain-specific corpora.Graph databases (Neo4j, ArangoDB) and graph-aware retrieval.Vector databases, hybrid search (BM25 + dense, rerankers).Familiarity with LLM observability tools: Langfuse, LangSmith, Arize, Braintrust, or in-house equivalents.Experience in healthcare, RCM, claims, or regulated domains.Experience with MCP or similar tool orchestration frameworks.Strong prompt-engineering and LLM evaluation skills. About YMinds.AI YMinds.AI is a technology and AI talent partner, helping organizations hire exceptional professionals across AI/ML, Data Science, Cloud Engineering, Full Stack Development, Product Engineering, and emerging technology domains. Using the proprietary EmployAbility.AI platform, YMinds.AI delivers pre-vetted, highly skilled talent to accelerate innovation and scale teams efficiently. Keywords Senior AI Engineer, ML Engineer, LLM Engineer, Large Language Models, Self-Hosted LLM, vLLM, SGLang, TensorRT-LLM, PyTorch, Hugging Face, Knowledge Graph, Embedding-Based Retrieval, RAG, Healthcare AI, Revenue Cycle Management, MCP, Prompt Engineering, Denial Management, Claim Automation, Medical Coding, ICD-10, CPT, HCPCS, NCCI Hashtags #AIEngineer #MLEngineer #LLM #HealthcareAI #RevenueCycleManagement #SelfHostedLLM #KnowledgeGraph #PromptEngineering #RAG #PyTorch #HuggingFace #MedicalCoding #DenialManagement #AIJobs #TechHiring #YMindsAI About the Role Our client is seeking a Senior AI/ML Engineer to build and operate production-grade Large Language Model (LLM) systems, knowledge graphs, and embedding-based retrieval pipelines for healthcare revenue cycle management. The ideal candidate will have hands-on experience deploying self-hosted LLMs, designing embedding and graph-based retrieval, and building evaluation and monitoring pipelines. This role offers full ownership of applied LLM infrastructure in a regulated healthcare environment, solving complex domain-specific problems spanning coding, claim edits, denials triage, appeal generation, and payer-rule reasoning. Key Responsibilities Deploy, fine-tune, and operate self-hosted LLMs such as Llama, Qwen, MedGemma, using vLLM, SGLang, TensorRT-LLM.Own fine-tuning workflows (SFT, LoRA, QLoRA, DPO) on clinical notes, claims, and payer-rule data.Optimize GPU usage, latency, batching, and cost for production LLM inference.Design, maintain