Role & responsibilities

Model Development & AI-Assisted Coding Expansion

- Design, train, and fine-tune models that power AI-assisted medical coding.
- Build confidence-scoring and human-override logic so coding suggestions meet audit and quality-review standards.
- Run structured evaluation cycles precision/recall, edge-case testing, and specialty-specific accuracy benchmarking before features ship.
- Collaborate with Medical Coders and QA to validate model output against real production coding decisions. Platform & Pipeline Engineering
- Build and maintain production ML pipelines from feature engineering through inference in partnership with the ML Ops/Data Engineer.
- Contribute to the Unify Platform's canonical data layer, ensuring model-ready data is clean, versioned, and reproducible.
- Optimize inference cost and latency across the model-serving stack.
- Own model monitoring drift detection, performance-degradation alerts, and retraining triggers. AI Governance & Explainability
- Document model behavior, confidence thresholds, and known limitations in a form that satisfies audit and compliance review.
- Implement explainability tooling so coding and automation decisions can be traced and justified to clients and auditors.
- Support the AI Lead's ISO 42001 and AI governance initiatives with technical documentation and evidence. Cross-Functional Delivery
- Partner with the Agentic AI Engineer on features that combine model output with agentic and automated workflows.
- Mentor Junior AI Engineers on model development practices, code quality, and production readiness.




- Support demo preparation and technical walkthroughs for prospective clients evaluating RevHC's AI capabilities.

NON-NEGOTIABLE REQUIREMENTS

- 5+ years of hands-on experience building and deploying machine learning or AI/LLM-based systems in production.
- Solid Python skills and fluency with ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face, or equivalent).
- Practical experience with LLM-based systems prompt engineering, fine-tuning, or RAG architectures.
- Experience with MLOps fundamentals model versioning, CI/CD for ML, and monitoring in production.
- Strong SQL and data-handling skills — comfortable working directly with large, messy datasets.
- Demonstrated experience taking a model from prototype to production, not just notebooks.
- Excellent written English for technical documentation and cross-functional communication.

STRONG DIFFERENTIATORS Experience in US healthcare / RCM (medical coding, claims, or clinical NLP) is a strong plus — not mandatory.

- Exposure to ICD-10/CPT coding taxonomies or clinical/medical text processing.
- Experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, or equivalent).
- Familiarity with AI explainability tooling and audit-trail design for regulated environments.
- Prior work in a healthcare IT product company, understanding release cycles and product-debt tradeoffs.

Preferred candidate profile 5–8 years in AI/ML or applied engineering roles, with production ML deployment experience .

Mentors Junior AI Engineers; coordinates with the Agentic AI Engineer AI Lead, ML Ops/Data Engineer, Data Lead, QA Engineers

Location: Hyderabad, Onsite

Interested candidates kindly reach out

HR Keerthi

(phone hidden)

[email protected]

📌 Senior AI Engineer/ Senior Artificial Intelligence engineer (Hyderabad)
🏢 Data Marshall
📍 Hyderabad