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Sr. Principal Machine Learning Engineer

Eli Lilly

Bengaluru

6+ years

Today

$47.0K–75.9K/yr

Full-time

Onsite

Skills Required LLM

RAG

LangChain

Agentic AI

Gen AI

Embeddings

Prompt Engineering

Fine-tuning

Vector Database

Transformer

LoRA

QLoRA model distillation

Small Language Models

Python

Description Lilly is a global pharmaceutical company focused on developing AI systems to improve patient outcomes. This senior role involves building scalable AI/ML solutions and influencing engineering practices across teams.

Company: Lilly

Role: Senior AI/ML Engineer

Experience

- 6+ years of experience building and deploying production machine learning or AI solutions
- 3+ years working with generative AI technologies including LLMs, RAG architectures, embeddings, prompt engineering, model evaluation, and agentic workflows

Qualification

- M.Tech, MS, or higher degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative discipline preferred

Responsibilities

- Architect and build production-grade machine learning, deep learning, generative AI, and agentic AI systems
- Lead technical design for solutions involving retrieval-augmented generation, embeddings, semantic search, LLM orchestration, Text2SQL, recommendation systems, predictive modeling, and intelligent workflow automation
- Design scalable AI services and APIs integrating with enterprise data platforms, business applications, CRM ecosystems, and downstream workflows
- Design, train, fine-tune, and deploy neural network models including Transformers, CNNs, RNNs, and LSTMs
- Develop deep learning solutions prioritizing performance, explainability, scalability, and production readiness
- Apply frameworks such as PyTorch and TensorFlow to build and productionize neural network models
- Build modular, reusable, observable, secure, and maintainable AI solutions aligned with enterprise technology patterns
- Own full lifecycle of AI/ML delivery including problem framing, data preparation, feature engineering, experimentation, model training, prompt and context design, evaluation, deployment, monitoring, and continuous improvement
- Develop and productionize NLP and LLM capabilities including RAG, prompt engineering, model adaptation, fine-tuning, and response quality evaluation
- Train and fine-tune neural network and language models using full fine-tuning, instruction tuning, domain adaptation, and parameter-efficient techniques such as LoRA and QLoRA
- Apply model distillation techniques to compress large models into smaller efficient models balancing accuracy, latency, cost, and operational constraints
- Design, train, evaluate, and deploy Small Language Models as lightweight alternatives for latency-sensitive or resource-constrained use cases
- Evaluate trade-offs between foundation models, fine-tuned models, distilled models, and Small Language Models for business and governance requirements
- Implement model evaluation frameworks for predictive, generative,



and retrieval-based systems including accuracy, relevance, hallucination risk, latency, cost, and robustness
- Monitor production performance, data drift, model drift, failures, and usage patterns and drive remediation or optimization
- Build and operate scalable data and feature pipelines using cloud-native and enterprise data platforms
- Implement MLOps and LLMOps practices including CI/CD, model registry, experiment tracking, version control, reproducibility, automated testing, observability, lineage, and auditability
- Work with platforms such as Databricks, SageMaker, Azure or AWS services, Kubernetes, Docker, MLflow
- Partner with data engineering and platform teams to ensure data quality, governance, lineage, access control, and operational reliability
- Work closely with product owners, business stakeholders, data scientists, architects, compliance, and engineering teams to translate business opportunities into AI/ML product capabilities
- Shape technical approaches for use cases across HCP targeting, field engagement, content recommendation, analytics, workflow automation, and decision intelligence
- Communicate model behavior, design tradeoffs, risks, and recommendations to technical and non-technical audiences
- Influence product roadmap decisions by bringing AI/ML feasibility, scalability, governance, and value considerations into planning
- Apply secure-by-design, privacy-by-design, and responsible AI principles across the AI/ML lifecycle
- Ensure AI systems have 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
- Create and maintain architecture, design, evaluation, and support documentation for production AI systems
- Act as senior technical contributor and guide for AI/ML engineers, data scientists, and product teams
- Promote reusable patterns, engineering standards, best practices, and evaluation approaches across teams
- Review solution designs, code, architecture choices, and operational patterns to improve quality and consistency
- Support team capability building through mentoring, technical coaching, knowledge sharing, and hands-on examples

Additional Responsibilities

- Ensure AI systems meet standards for security, privacy, and governance
- Maintain documentation and technical governance habits
- Communicate complex technical concepts to technical and non-technical audiences
- Translate ambiguous business challenges into scalable technical solutions delivering measurable business value




- Lead technical initiatives and influence architectural decisions across teams

Nice To Have

- Experience with commercial pharma, healthcare analytics, CRM, customer engagement, decision-support platforms, HCP data, field engagement workflows, targeting/orchestration, or Veeva-related ecosystems
- Experience with enterprise AI/data platforms such as Databricks, SageMaker, Azure ML, or similar
- Experience with GenAI patterns such as multi-agent systems, tool use, Text2SQL, semantic search, knowledge graphs, hybrid retrieval, reranking, context engineering, and LLM evaluation
- Experience with foundation model fine-tuning, instruction tuning, LoRA, QLoRA, model compression, model distillation, quantization, and Small Language Model development
- Experience evaluating and optimizing AI systems across quality, latency, throughput, scalability, and cost dimensions
- Experience with FastAPI, Flask, or similar frameworks for model/API serving
- Experience with Kubernetes, Docker, GitHub Actions, MLflow, vector databases, Spark, PySpark, or lakehouse architectures
- Familiarity with responsible AI practices such as explainability, bias detection, grounded generation, guardrails, auditability, and human-in-the-loop design
- Experience creating executive-facing dashboards, model insights, or AI observability views using tools such as Power BI, Plotly, Dash, or similar
- Solid documentation, design-review, and technical governance habits

More Skills Machine Learning Engineering, Small Language Models (SLMs), Deep Learning and Neural Network Architecture Design, Model Evaluation and Error Analysis, Generative AI and LLM Engineering, SQL, PySpark, Databricks, SageMaker, AWS, Azure, Agentic AI and Multi-Agent Workflows, MLOps, LLMOps, Context Engineering, CI/CD, Docker, Kubernetes, Git, PyTorch, TensorFlow, Model Monitoring, Drift Detection, Transformer Models, CNNs, RNNs, LSTMs, API Development, AI Service Integration, Instruction Tuning, Domain Adaptation, Responsible AI, Security, Privacy, Governance, Parameter-Efficient Fine-Tuning, Business-facing Technical Leadership, Compression, Quantization

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