Job Summary

You will work closely with product managers, software engineers, data scientists, architects, and business stakeholders to develop production-ready AI systems leveraging machine learning, deep learning, large language models, retrieval-augmented generation (RAG), Small Language Models (SLMs), intelligent agents, and contemporary ML Ops practices.

This is a senior individual-contributor role that stays hands-on keyboard. You will spend the majority of your time building - architecting, coding, training, evaluating, and deploying - while providing light technical mentoring to a small number of engineers within your squad. Success means shipping scalable, reliable, well-governed AI solutions that deliver real user value across the LVA portfolio.

What You'll Build

- Agentic and LLM systems for market access, real-world evidence, and value/access workflows - multi-agent orchestration, tool use, and intelligent workflow automation.

- RAG and Text2SQL over real-world data (RWD), market access datasets, and enterprise knowledge sources - embeddings, semantic search, hybrid retrieval, and reranking.

- Predictive and ML models supporting payer, formulary, book-of-business, and health-outcomes analytics.

- Production AI services and APIs that integrate with enterprise data platforms, business applications, and downstream product workflows.

Key Responsibilities

AI/ML Solution Architecture and Engineering

- Architect and build production-grade ML, deep learning, generative AI, and agentic AI systems for LVA market access and engagement use cases.

- Lead technical design for solutions involving RAG, embeddings, semantic search, LLM orchestration, Text2SQL, recommendation, predictive modeling, and workflow automation.

- Design scalable AI services and APIs that integrate with enterprise data platforms and product workflows.

- Design, train, fine-tune, and deploy neural network models (Transformers, CNNs, RNNs, LSTMs as applicable)



using PyTorch (TensorFlow a plus), with a focus on performance, generalization, explainability, scalability, and production readiness.

- Build modular, reusable, observable, secure, and maintainable solutions aligned with enterprise technology patterns.

End-to-End Model and GenAI Delivery

- Own the full AI/ML lifecycle: problem framing, data prep, feature engineering, experimentation, training, prompt and context design, evaluation, deployment, monitoring, and continuous improvement.

- Develop and productionize NLP and LLM capabilities - RAG, prompt engineering, model adaptation, and fine-tuning where appropriate (full, instruction, domain adaptation, and parameter-efficient methods such as LoRA/QLoRA).

- Apply model distillation, compression, and quantization to balance accuracy, latency, cost, and operational constraints; evaluate and deploy SLMs for latency-sensitive, cost-sensitive, or specialized use cases.

- Evaluate trade-offs across foundation, fine-tuned, distilled, and small language models to pick the right architecture for business, operational, and governance needs.

- Implement evaluation frameworks for predictive, generative, and retrieval systems (accuracy, relevance, groundedness, hallucination risk, latency, cost, robustness).

- Monitor production performance, data drift, model drift, failures, and usage; drive remediation and optimization.

Data, Platform, and MLOps Engineering

- Build and operate scalable data and feature pipelines on cloud-native and enterprise data platforms.

- Implement MLOps/LLMOps practices: CI/CD, model registry,



experiment tracking, version control, reproducibility, automated testing, observability, lineage, and auditability.

- Work primarily with AWS and Databricks (SageMaker, MLflow, Kubernetes, Docker, and related enterprise-approved tooling).

- Partner with data engineering and platform teams to ensure data quality, governance, lineage, access control, and operational reliability.

Business Partnership and Product Impact

- Partner with product owners, business stakeholders, data scientists, architects, compliance, and engineering to translate market access opportunities into AI/ML product capabilities.

- Shape technical approaches for high-value use cases across analytics, decision intelligence, evidence generation, and workflow automation.

- Communicate model behavior, trade-offs, risks, and recommendations clearly to technical and non-technical audiences.

- Bring practical AI/ML feasibility, scalability, governance, and value considerations into roadmap planning.

Responsible AI, Security, and Governance

- Apply secure-by-design, privacy-by-design, and responsible AI principles across the lifecycle.

- Ensure appropriate controls for explainability, traceability, bias awareness, grounding, auditability, and human oversight.

- Collaborate with governance, compliance, quality, and risk partners to meet standards for data use, reliability, documentation, and operational readiness; maintain architecture, design, evaluation, and support documentation for production AI systems.

Technical Mentoring (light)

- Provide hands-on technical guidance to a small number of AI/ML engineers within your squad.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr Principal- AI / ML Engineer, Market Access Intelligence (Bengaluru)
🏢 Eli Lilly
📍 Bengaluru