Sr. Principal Machine Learning Engineer
Eli Lilly and Company7 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Architect and build production-grade machine learning, deep learning, generative AI, and agentic AI systems for commercial and field engagement use cases.
- Design solutions involving RAG, embeddings, semantic search, LLM orchestration, Text2SQL, recommendation systems, predictive modeling, and intelligent workflow automation.
- Design, train, fine-tune, evaluate, and deploy neural network, language, and Small Language Models using techniques including instruction tuning, domain adaptation, LoRA, QLoRA, distillation, compression, and quantization.
- Own the end-to-end AI/ML lifecycle from problem framing and data preparation through deployment, monitoring, drift detection, and continuous improvement.
- Build scalable data, feature, AI service, API, MLOps, and LLMOps capabilities with monitoring, observability, experiment tracking, model management, testing, lineage, and auditability.
- Partner with product, business, data engineering, platform, architecture, compliance, quality, and risk teams to deliver governed AI capabilities.
- Apply responsible AI, privacy, security, explainability, grounding, bias awareness, human oversight, and governance practices.
- Lead technical initiatives, review designs and code, establish reusable engineering patterns, influence architecture, and mentor AI/ML engineers and data scientists.
Requirements
- At least 6 years of experience building and deploying production machine learning or AI solutions.
- At least 3 years of experience with generative AI technologies, including LLMs, RAG architectures, embeddings, prompt engineering, model evaluation, and agentic workflows.
- Strong proficiency in Python and SQL.
- Hands-on experience building and fine-tuning deep learning models with PyTorch; TensorFlow experience is preferred.
- Experience with AI/ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, and DSPy or equivalent technologies.
- Experience deploying AI services on AWS, Azure, or GCP.
- Experience implementing MLOps or LLMOps practices, including monitoring, observability, experiment tracking, model management, automated testing, and CI/CD.
- Strong software engineering fundamentals in API development, system design, version control, code reviews, and testing.
- Understanding of neural network architectures, transfer learning, model optimization, evaluation, and production deployment.
- Experience working with structured, semi-structured, and unstructured data at scale.
- Ability to communicate complex technical concepts and translate ambiguous business challenges into scalable solutions.
- Demonstrated experience leading technical initiatives and influencing architecture across teams.
- Preferred experience includes healthcare or pharmaceutical analytics, CRM and customer engagement platforms, HCP data, Veeva ecosystems, enterprise AI/data platforms, multi-agent systems, semantic search, knowledge graphs, hybrid retrieval, reranking, context engineering, and LLM evaluation.
- Preferred experience includes FastAPI, Flask, Kubernetes, Docker, GitHub Actions, MLflow, vector databases, Spark, PySpark, lakehouse architectures, Power BI, Plotly, and Dash.
- A preferred M.Tech, MS, or higher degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative discipline.
Tech Stack
Apache SparkAWSAzureDatabricksDockerFastAPIFlaskGitGitHub ActionsGoogle Cloud PlatformKubernetesMLflowPythonPyTorchscikit-learnSQLTensorFlow
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