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This role is for one of our client companies — a VC-backed startup that has raised $11M USD in funding.

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Apply once and, if selected, get access to up to 20 remote and onsite interview opportunities.

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CodeRound AI matches the top 5% tech talent with the fastest-growing, VC-funded AI startups across Silicon Valley and India.

Top-tier product startups across the US, UK, EU, UAE, and India have hired top engineers through CodeRound.

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We are seeking a Senior AI Engineer to own the applied LLM and retrieval infrastructure for a high-growth healthcare technology startup. In this role, you will design and scale production-grade AI systems, leveraging open-source models to automate complex clinical workflows.

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- Self-Hosted LLM Infrastructure: Deploy, fine-tune, and operate open-source LLMs as the primary inference stack, optimizing GPU utilization, throughput, latency, batching, and inference costs.
- Model Fine-Tuning: Own end-to-end fine-tuning workflows using SFT, LoRA, QLoRA, and DPO to adapt models to clinical notes, claims, and payer-specific data.
- Knowledge Graph Engineering: Design and maintain knowledge graphs representing medical coding standards, healthcare policies, payer rules, and relationships across coding systems.
- Retrieval Systems: Build and optimize embedding-based retrieval pipelines using chunking, embedding models, hybrid search, vector databases, and reranking.
- Grounded AI Systems: Combine graph traversal and dense retrieval to ensure coding recommendations, claim edits, and appeal responses are backed by reliable and auditable evidence.
- Evaluation Infrastructure:



Build continuous evaluation pipelines and offline benchmarks using human-validated datasets to assess model accuracy, retrieval quality, and system reliability.
- Production Monitoring: Monitor hallucinations, model drift, regressions, and output quality through shadow deployments, cohort-level accuracy tracking, and production observability.
- LLM Application Development: Design prompts, context pipelines, structured outputs, and function-calling workflows for medical coding, denial classification, claim edits, and appeal generation.

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- 4+ years of experience in AI/ML Engineering, Machine Learning, or Applied AI, including at least 6 months of hands-on production LLM experience.
- Strong proficiency in Python, PyTorch, and Hugging Face Transformers.
- Hands-on experience deploying, serving, and operating self-hosted open-source LLMs using vLLM, SGLang, TensorRT-LLM, or equivalent inference frameworks.
- Robust understanding of LLM inference optimization, including GPU utilization, batching, KV-cache management, throughput, latency, and serving costs.
- Experience designing embedding-based retrieval systems and/or knowledge graphs for grounded LLM applications.
- Strong understanding of RAG architectures, including document chunking, embedding generation, hybrid search, retrieval optimization, and reranking.

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- Build and ship production AI systems with complete technical ownership.
- Create massive real-world impact at a well-funded Series A startup.
- Accelerate your career growth by solving complex, unsolved engineering challenges.
- Work alongside an elite team of ambitious and highly driven builders.

📌 AI Engineer (Upto 70LPA) (India)
🏢 CodeRound AI
📍 India