SENIOR SOFTWARE ENGINEER - TECHNOLOGY

Position Dossier – Senior Software Engineer

About Us

Omega Healthcare Management Services® (Omega Healthcare) is an AI-driven healthcare solutions company that partners across the healthcare ecosystem to deliver breakthrough results by reimagining and elevating revenue operations.

Powered by the Omega Digital Platform®, our agentic AI engine leverages adaptive intelligence to drive automation, complemented by deep human expertise to help optimize performance and deliver sustained financial and clinical outcomes—while enhancing patient satisfaction.

With a strong global presence spanning four countries, our team of 30,000+ dedicated professionals collaborate with hospitals, physician groups, and healthcare providers to streamline operations, improve financial performance and elevate patient care outcomes.

We combine our deep domain expertise, advanced technology, and a customer-centric approach in delivering innovative and scalable healthcare support solutions.

Position Title: Senior Software Engineer

Department: Engineering & Technology | Function: AI/ML Platform Engineering/Data Science

Role Summary

The Senior Software Engineer is a hands-on technical expert responsible for designing, developing, fine-tuning, and evaluating advanced Machine Learning (ML), Deep Learning (DL), Small Language Models (SLMs), and complex Retrieval-Augmented Generation (RAG) architectures. This role focuses on translating enterprise data into measurable business impact by building robust hybrid retrieval pipelines (including GraphRAG), developing agentic scaffolding (harness engineering), and operationalizing state-of-the-art AI research into reliable production systems.

Key Responsibilities

• RAG & GraphRAG Architecture: Design and optimize advanced retrieval pipelines. Move beyond basic vector search by implementing GraphRAG: extracting entities/relationships into knowledge graphs (e.g., Neo4j), applying community detection algorithms, and enabling map-reduce summarization for corpus-wide reasoning and complex multi-hop queries.
• Agent Harness Engineering: Build the critical infrastructure (the "harness") around language models to ensure reliable agentic behavior. Own the execution runtime, including tool routing, short/long-term memory management, state persistence, deterministic guardrails, and feedforward/feedback loops for autonomous error correction.
• Advanced Model Development: Design, build, and deploy specialized SLM and DL solutions. Apply deep statistical rigor to address class-imbalance, long-tail coverage, and model calibration.
• NLP & Language Modeling: Build solutions utilizing Transformers, embeddings, and language-model fine-tuning. Adapt base SLMs to domain-specific use cases using multi-adapter strategies (LoRA / QLoRA / PEFT).
• Evaluation & Retrieval Quality: Drive eval-driven iteration for both agents and retrieval systems. Establish benchmarks for generative outputs, context relevance, and GraphRAG path accuracy. Maintain human-curated "golden" evaluation datasets under double-blind, multi-annotator adjudication.
• Data Engineering & Vector/Graph Storage: Own large-scale extraction pipelines. Manage both Vector Databases for semantic search and Graph Databases for explicit relationship mapping, ensuring strict train/eval separation.
• Technical Execution: Write expert-level, highly optimized Python code, Graph query languages (Cypher/Gremlin), and complex SQL queries to interact with large production data stores.

Required Skills & Expertise

• RAG, GraphRAG & Vector Search: Hands-on experience with Microsoft GraphRAG or custom GraphRAG implementations. Proficiency in Knowledge Graphs (Neo4j, Amazon Neptune), Graph query languages (Cypher, Gremlin), Vector Databases (Pinecone, Milvus), and Hybrid Search.
• Agent Harnessing & Orchestration: Frameworks for multi-agent workflows (LangGraph, LlamaIndex), memory architecture, tool integration (MCP), and safety guardrails (Guardrails AI, NeMo Guardrails).
• Classical ML & Deep Learning: Random Forest, SVM, Boosting/Bagging, CNN, RNN, LSTM, Transformer architectures.
• NLP & LLM/SLM Fine-Tuning: Hands-on experience with LoRA, QLoRA, PEFT, quantization, and model distillation.
• Frameworks & Tooling: PyTorch, Hugging Face (Transformers/TRL/PEFT), unsloth/bitsandbytes.
• Programming: Expert-level Python programming, advanced SQL, and data pipeline optimization.
• Evaluation & Metrics: RAGAS, TruLens, LLM-as-a-judge frameworks, and robust quantitative metric tracking.

Preferred / Nice to Have

• Domain Knowledge: Strong preference for experience within the Healthcare and Medical domain, specifically including Medical Coding knowledge.
• Experience with RHLF/GRPO-style reward modeling.
• Contributions to open-source ML, RAG, or Agent tooling / published research.
• Constrained-hardware or edge-GPU model optimization.
• Computer Vision exposure.

Educational Qualification

• BE / BTech / ME / MTech / MCA in Computer Science, Statistics, Mathematics, or a related field.
• PhD preferred for candidates with a strong research focus.

Experience

• 5–8+ years of applied experience in Data Science, Machine Learning, and Deep Learning.
• 2+ years of hands-on experience building, fine-tuning, and evaluating production RAG, GraphRAG, and LLM/agentic workflows.
• Proven track record of developing and deploying advanced NLP models to production environments.

Work Schedule

Day Shift