AI Engineer - Healthcare & Revenue Cycle Management (RCM) at AI diffusion in asset-heavy & regulated industries · Chennai · 2 - 5 years · Upto ₹26L / yr · Posted 21 Oct 2026
AI Engineer - Healthcare & Revenue Cycle Management (RCM)
at AI diffusion in asset-heavy & regulated industries
Python
Django
FlaskAbout the company
We are an AI diffusion company. We partner with midmarket companies in asset-heavy, regulated industries to co-build deeply industry-specific agentic and machine learning solutions, which then get diffused across the industry through revenue-share and equity-linked models.
Our Past
We have built and sold two companies, one of which was an AI company. Our founders and leaders are Math PhDs, Ivy League University Alumni, Ex-Googlers, and successful entrepreneurs.
Team
Our team of 20+ employees consists of data scientists, AI/ML Engineers, and mathematicians from top engineering and research institutes such as IITs, CERN, IISc, UZH. Our team includes academicians, IBM Research Fellows, and former founders. This role focuses on healthcare and RCM, working alongside our AI/ML engineers, data scientists and domain specialists.
Work you’ll do
We are looking for an experienced AI engineer with hands-on experience in healthcare and revenue cycle management (RCM). You will build and deploy AI agents, automations and data workflows for healthcare operations, with a focus on the US market.
The work includes payer-portal automation for claim status and EOB retrieval, voice agents for payer follow-up, analysis of claim journeys and bottlenecks, and AI-assisted insights into practice performance. You will work directly with billing and operations teams, owning delivery from understanding the workflow through deployment, evaluation and ongoing improvement.
Responsibilities
- Own AI agent and automation workflows from requirements through design, development, deployment and monitoring.
- Build agents that work across payer portals, healthcare documents, practice-management systems, EHR-connected tools and voice channels.
- Turn fragmented healthcare data into validated, structured inputs for automation and analysis.
- Work directly with billing and operations teams to understand workflows, resolve exceptions and improve systems based on user feedback.
- Evaluate workflow accuracy and completion rates using representative test sets. Monitor failures and operating costs, and implement retries, exception queues and human escalation
- Implement approved privacy and security controls for sensitive healthcare data, including access restrictions, audit trails and safeguards against exposing patient information in logs or model inputs.
- Diagnose and improve failures across data ingestion, integrations, agent execution and user-facing workflows.
- Measure operational outcomes, such as time saved, reduced manual effort and faster turnaround, with the teams using the systems.
Who you are
You are a passionate and results-oriented engineer who is driven by the potential of AI/ML to revolutionize processes, enhance products, and ultimately improve user experiences. You thrive in dynamic environments and are comfortable navigating ambiguity. You possess a strong sense of ownership and are eager to take initiative, advocating for your technical decisions while remaining open to feedback and collaboration.
You are adept at working with real-world, often imperfect data, and have a proven ability to develop, refine, and deploy AI/ML models into production in a cost-effective and scalable manner. You are excited by the prospect of making US healthcare operations faster and more reliable, for the people who run them and the patients they serve.
Skills & Requirements
Must have
- At least 2 years of professional AI engineering experience, including deploying and supporting systems used in real operations. Internships and academic projects alone do not meet this requirement.
- Hands-on experience building AI or automation solutions for healthcare RCM workflows, such as claim status checks, accounts receivable follow-up, denial management, payment posting or payer interactions.
- Practical understanding of how billing teams work, how claims move through the revenue cycle, and how incomplete data and exceptions affect execution.
- Experience deploying and supporting an LLM-based application or AI agent in production, using capabilities such as retrieval-augmented generation (RAG), tool calling or multi-step workflows. Ability to explain your engineering contribution, how the system was evaluated, what failed and how you addressed it.
- Strong production Python and SQL skills, experience integrating APIs, and working knowledge of JavaScript/TypeScript for browser automation or application integration.
- Experience automating workflows across APIs, browsers or user interfaces, using tools such as Playwright, Selenium or UiPath, with error handling and human escalation where needed.
- Experience deploying and monitoring applications on at least one cloud platform—AWS, Azure or GCP—using containers, version control and automated testing.
- Experience handling imperfect inputs such as scanned PDFs, inconsistent portal layouts or incomplete records, with validation and exception handling.
- Experience working directly with billing or operations teams, improving systems based on their feedback, and demonstrating outcomes such as time saved, claims processed or reduced turnaround time.
- Experience handling sensitive healthcare data through appropriate access controls, secure logging and approved data-handling practices.
Preferred experience
- Experience delivering solutions for US healthcare providers, RCM companies or payer-facing operations, including working with US billing teams and payer workflows.
- Experience with practice-management or EHR integrations, payer portals, explanation of benefits (EOB) documents or electronic remittance advice (ERA) data.
- Experience analyzing claim histories, revenue-cycle bottlenecks or practice performance.
- Eperience building voice agents or integrating speech-to-text and text-to-speech services is a plus.
- Familiarity with Model Context Protocol (MCP) integrations.
- Experience with Kubernetes, serverless applications or event-driven architectures.
Working with us
We are a young company, but we believe strongly in thinking long-term, while acting with urgency. Our ethos is rooted in innovation, efficiency and high-quality outcomes. We believe the future of work is AI-augmented and boundaryless.
Here are some of our guiding principles:
- Think in decades. Act in hours. As an independent company, our moat is time. While our decisions are for the long-term horizon, our execution will be fast – measured in hours and days, not weeks and months.
- Own the canvas. Throw yourself in to build, fix or improve – anything that isn’t done right, irrespective of who did it. Be selfish about improving across the organization – because once the rot sets in, we waste years in surgery and recovery.
- Use data or don’t use data. Use data where you ought to but not as a ‘cover-my-back’ political tool. Be capable of making decisions with partial or limited data. Get better at intuition and pattern-matching. Whichever way you go, be mostly right about it.
- Avoid work about work. Process creeps on purpose, unless we constantly question it. We are deliberate about committing to rituals that take time away from the actual work. We truly believe that a meeting that could be an email, should be an email and you don’t need a person with the highest title to say that out loud.
- High revenue per person. We work backwards from this metric. Our default is to automate instead of hiring. We multi-skill our people to own more outcomes than hiring someone who has less to do. We don’t like squatting and hoarding that comes in the form of hiring for growth. High revenue per person comes from high quality work from everyone. We demand it.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Ship to production
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- REST API development with FastAPI (or Flask/Django with a willingness to move to FastAPI).
- Git, code review discipline, and the ability to write code someone else can maintain.
- Comfortable in Linux and on the command line.
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Strongly preferred
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Orchestration & pipelines
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- LLM orchestration frameworks (LangGraph, LlamaIndex, Haystack) with the judgement to know when plain Python is a better answer.
- Structured output enforcement: Instructor, Outlines, XGrammar, JSON schema / tool-use modes.
Evaluation & observability
- Building golden datasets and regression suites for extraction tasks.
- Eval tooling: promptfoo, DeepEval, Ragas, or in-house harnesses.
- LLM tracing and monitoring: Langfuse, Arize Phoenix, LangSmith, OpenTelemetry.
Nice extras
- Rule engines and policy evaluation (Open Policy Agent / Rego, Drools, rule-engine).
- Experience in fintech, lending, insurance or accounting documents.
- Handling of PII and data-security practices in document pipelines.
- Contributions to open-source ML or document-processing projects.
Why join us
- Real production ownership from month one your work goes to actual users, not a demo.
- Genuinely hard technical problems in document AI, not wrappers over an API.
- Small team, short decision cycles, direct access to leadership.
- Budget and freedom to evaluate and adopt new open-source models as they land.
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Python🚀 Hiring: Python + Gen AI Developer
📍 Location: Bengaluru
💼 Experience: 4–6 Years
Mandatory Skills:
✅ Python
✅ Generative AI / Gen AI
✅ LLMs / AI Application Development
✅ Python Backend / API Development
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🔹 Develop scalable Python applications
🔹 Build and integrate Gen AI/LLM solutions
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🔹 Collaborate with cross-functional teams
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📩 Interested candidates can share their updated resume.
#Hiring #PythonDeveloper #GenAI #GenerativeAI #LLM #AIJobs #BengaluruJobs #TechHiring #ITJobs
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- Ability to translate business requirements into practical AI/ML solutions.
- Strong software engineering and debugging capabilities.
- Ability to work independently as well as collaboratively in a cross-functional environment.
- Good communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.
Keywords
AI Engineer | ML Engineer | Machine Learning | Generative AI | LLM | Python | NLP | Deep Learning | RAG | Prompt Engineering | AI Agents | Azure OpenAI | AWS Bedrock | MLOps | TensorFlow | PyTorch | Scikit-learn | Vector Database | Cloud AI
Location: Gurugram
Work mode: Hybrid
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon