{"id":1272942,"url":"https://alion.io/job/hyresnap-senior-aiml-engineer","title":"Senior AI/ML Engineer","company":{"id":3808927,"name":"HyreSnap","domain":"hyresnap.com","url":"https://alion.io/company/hyresnap","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":49000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"ArangoDB","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"Braintrust","optional":false},{"name":"DPO","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Hugging Face","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"KV Cache","optional":false},{"name":"Langfuse","optional":false},{"name":"LangSmith","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Neo4j","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"SFT","optional":false},{"name":"SGLang","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"vLLM","optional":false},{"name":"PEFT","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-08-29T12:52:26Z","employer_posted_date":null,"last_verified_at":"2026-08-29T12:52:26Z","board_verified":false,"closed_at":null,"days_open":46,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":46},"description":"About the Role :\n\nWe are hiring a Senior AI/ML Engineer to own the end-to-end applied LLM, retrieval, and evaluation layer of our healthcare AI platform. You will build production systems that automate mid- and end-revenue cycle workflows for US healthcare spanning coding, claim edits, denial triage, appeal generation, and payer-rule reasoning. This is a production engineering role focused on building scalable, auditable, and cost-efficient LLM systems in a regulated healthcare environment.\n\nSelf-Hosted LLM Infrastructure :\n\n- Deploy, fine-tune, and operate open-source models (Llama, Qwen, MedGemma, and successors) as our primary inference stack.\n\n- Work with vLLM / SGLang / TensorRT-LLM for serving at scale, with disciplined attention to throughput, tail latency, batching, KV-cache, and GPU economics.\n\n- Own fine-tuning workflows end-to-end (SFT, LoRA, QLoRA, DPO) on clinical notes, claims, and payer-rule data.\n\n- Optimize GPU usage, latency, batching, and cost; make build-vs-buy and hosted-vs-self-hosted trade-offs explicit and measured.\n\nKnowledge Graphs and Embedding-Based Retrieval :\n\n- Design and maintain the knowledge graph encoding ICD-10-CM, CPT, HCPCS, modifiers, HCC, NCCI edits, LCD/NCD policies, and payer-specific rules.\n\n- Build embedding-based retrieval over clinical notes, historical claims, denial reasons, and payer-policy corpora, including chunking, embedding model selection, hybrid search, and reranking.\n\n- Combine graph traversal and dense retrieval so every coded line, scrubbed edit, and appeal response is grounded in auditable evidence.\n\n- Own ingestion, versioning, and quality of underlying knowledge sources (CMS, AHA, AMA, NCCI, payer bulletins).\n\nEvaluation and Monitoring :\n\n- Build continuous evaluation pipelines that gate every model, prompt, retrieval, and graph change before production.\n\n- Run offline eval suites grounded in coder- and biller-validated labels; use LLM-as-judge where appropriate, calibrated against human ground truth.\n\n- Monitor drift, hallucinations, regressions, and output quality in production; operate shadow-mode rollouts and per-cohort accuracy tracking.\n\n- Track business metrics: chart-level and opportunity-level coding accuracy, denial rate impact, clean-claim rate, cost per chart, and end-to-end latency.\n\nLLM Systems and Prompt Engineering :\n\n- Design prompts and context pipelines for coding, claim edits, denial classification, and appeal drafting.\n\n- Implement structured outputs (JSON, function calling, constrained decoding) on top of the self-hosted stack.\n\n- Apply RAG over medical coding standards (CMS, ICD-10 AHA, NCCI) and payer policies, grounded in the knowledge graph and embedding stores.\n\n- Treat prompts as a thin, well-versioned, well-evaluated layer, never the load-bearing piece.\n\nAgentic Workflows and Tooling MCP :\n\n- Build MCP servers for internal tools: code lookup, NCCI / rule checks, payer logic, eligibility, and denial classification.\n\n- Design multi-step agent workflows with audit trails and human-in-the-loop checkpoints for coder, biller, and AR-analyst review.\n\n- Define deterministic vs. LLM-based tool boundaries for reliability.\n\nRequirements :\n\n- 5+ years in ML/AI engineering, including 6+ months in production LLM systems.\n\n- Hands-on experience deploying and operating self-hosted LLMs (vLLM, SGLang, TensorRT-LLM, or equivalent).\n\n- Strong experience designing embedding-based retrieval and/or knowledge graphs for grounded LLM applications.\n\n- Demonstrated ownership of evaluation infrastructure, offline benchmarks, online monitoring, drift, and regression detection.\n\n- Strong Python + PyTorch + Hugging Face experience.\n\n- Production experience with monitoring, incidents, and system ownership.\n\nStrongly Preferred :\n\n- Fine-tuning experience (SFT, LoRA, QLoRA, DPO) on domain-specific corpora.\n\n- Experience with graph databases (Neo4j, ArangoDB, or equivalent) and graph-aware retrieval.\n\n- Experience with vector databases and hybrid search (BM25 + dense, rerankers).\n\n- Familiarity with LLM observability tools (Langfuse, LangSmith, Arize, Braintrust, or in-house equivalents).\n\n- Exposure to healthcare, RCM, claims, or other regulated domains.\n\n- Experience with MCP or similar tool-orchestration frameworks.\n\n- Strong prompt-engineering and LLM-evaluation instincts.\n\nSkills\nMachine Learning, Python, LLM, NLP, Artificial Intelligence, SQL, ETL, Observability Services, VectorDB","description_format":"text","description_chars":4424,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-26T00:07:01Z"}],"visa":[],"liveness":{"score":7,"band":"cold","label":"Long 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