{"id":121183,"url":"https://alion.io/job/rapidclaims-senior-ai-ml-engineer","title":"Senior AI / ML Engineer","company":{"id":46796,"name":"RapidClaims","domain":"rapidclaims.ai","url":"https://alion.io/company/rapidclaims","size_band":"11-50","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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":38},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"ArangoDB","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"Braintrust","optional":false},{"name":"DPO","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":"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":"PEFT","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":"Structured Outputs","optional":false},{"name":"TensorRT","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"vLLM","optional":false}],"status":"live","first_seen_at":"2026-08-25T07:41:33Z","employer_posted_date":null,"last_verified_at":"2026-08-25T07:41:33Z","board_verified":false,"closed_at":null,"days_open":57,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":57},"description":"We 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 (not research) focused on building scalable, auditable, and cost-efficient LLM systems in a regulated healthcare environment.\nThe core responsibilities for the job include the following:\nSelf-Hosted LLM Infrastructure:\nDeploy, fine-tune, and operate open-source models (Llama, Qwen, MedGemma, and successors) as our primary inference stack.\nWork with vLLM / SGLang / TensorRT-LLM for serving at scale, with disciplined attention to throughput, tail latency, batching, KV-cache, and GPU economics.\nOwn fine-tuning workflows end-to-end (SFT, LoRA, QLoRA, DPO) on clinical notes, claims, and payer-rule data.\nOptimize GPU usage, latency, batching, and cost; make build-vs-buy and hosted-vs-self-hosted trade-offs explicit and measured.\nKnowledge Graphs and Embedding-Based Retrieval:\nDesign and maintain the knowledge graph encoding ICD-10-CM, CPT, HCPCS, modifiers, HCC, NCCI edits, LCD/NCD policies, and payer-specific rules and the relationships between them.\nBuild embedding-based retrieval over clinical notes, historical claims, denial reasons, and payer-policy corpora, including chunking, embedding model selection, hybrid search, and reranking.\nCombine graph traversal and dense retrieval so every coded line, scrubbed edit, and appeal response is grounded in auditable evidence.\nOwn ingestion, versioning, and quality of underlying knowledge sources (CMS, AHA, AMA, NCCI, payer bulletins).\nEvaluation and Monitoring:\nBuild continuous evaluation pipelines that gate every model, prompt, retrieval, and graph change before production.\nRun offline eval suites grounded in coder- and biller-validated labels; use LLM-as-judge where appropriate, calibrated against human ground truth.\nMonitor drift, hallucinations, regressions, and output quality in production; operate shadow-mode rollouts and per-cohort accuracy tracking (specialty, payer, chart type).\nTrack business metrics: chart-level and opportunity-level coding accuracy, denial rate impact, clean-claim rate, cost per chart, and end-to-end latency.\nLLM Systems and Prompt Engineering:\nDesign prompts and context pipelines for coding (CPT, ICD, HCC, E/M), claim edits, denial classification, and appeal drafting.\nImplement structured outputs (JSON, function calling, constrained decoding) on top of the self-hosted stack.\nApply RAG over medical coding standards (CMS, ICD-10 AHA, NCCI) and payer policies, grounded in the knowledge graph and embedding stores.\nTreat prompts as a thin, well-versioned, well-evaluated layer, never the load-bearing piece.\nAgentic Workflows and Tooling MCP:\nBuild MCP servers for internal tools: code lookup, NCCI / rule checks, payer logic, eligibility, and denial classification.\nDesign multi-step agent workflows with audit trails and human-in-the-loop checkpoints for coder, biller, and AR-analyst review.\nDefine deterministic vs. LLM-based tool boundaries for reliability. Reliability comes from knowing which is which.\nRequirements:\n5+ years in ML/AI engineering, including 6+ months in production LLM systems.\nHands-on experience deploying and operating self-hosted LLMs (vLLM, SGLang, TensorRT-LLM, or equivalent).\nStrong experience designing embedding-based retrieval and/or knowledge graphs for grounded LLM applications.\nDemonstrated ownership of evaluation infrastructure, offline benchmarks, online monitoring, drift, and regression detection.\nStrong Python + PyTorch + Hugging Face experience.\nProduction experience with monitoring, incidents, and system ownership.\nStrongly Preferred:\nFine-tuning experience (SFT, LoRA, QLoRA, DPO) on domain-specific corpora.\nExperience with graph databases (Neo4j, ArangoDB, or equivalent) and graph-aware retrieval.\nExperience with vector databases and hybrid search (BM25 + dense, rerankers).\nFamiliarity with LLM observability tools (Langfuse, LangSmith, Arize, Braintrust, or in-house equivalents).\nExposure to healthcare, RCM, claims, or other regulated domains.\nExperience with MCP or similar tool-orchestration frameworks.\nStrong prompt-engineering and LLM-evaluation instincts.","description_format":"text","description_chars":4394,"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":["Medical AI","Revenue Cycle & Medical Billing"],"lifecycle":[{"event":"open","at":"2026-08-25T07:41:33Z"}],"visa":[],"liveness":{"score":9,"band":"cold","label":"Long shot","p_open":0.4,"p_active":0.493,"p_room":0.45,"age_days":56,"expected_fill_days":31,"reasons":["seen:56","win:tail"],"computed_at":"2026-10-21T05:46:15Z"},"pay":null,"html_url":"https://alion.io/job/rapidclaims-senior-ai-ml-engineer","json_url":"https://alion.io/job/rapidclaims-senior-ai-ml-engineer.json","meta":{"generated_at":"2026-10-21T17:43:18Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":24988,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-22T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":46796},"rest":"https://alion.io/mcp/rest/get_company?id=46796"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Frapidclaims-senior-ai-ml-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Frapidclaims-senior-ai-ml-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Frapidclaims-senior-ai-ml-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/rapidclaims-senior-ai-ml-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Frapidclaims-senior-ai-ml-engineer"}]}