Advisor - AI/ML Engg, Lilly USA Commercial Technology
Eli Lilly and Company14 days ago
Bengaluru, IndiaSenior
Responsibilities
- Define enterprise AI blueprints, platform standards, governance frameworks, and responsible AI engineering guardrails.
- Evaluate and onboard AI/ML tooling across the approved AWS, Azure, Databricks, CATS, EDB, and AWB stack.
- Lead applied development of multi-agent systems, autonomous orchestration, LLM-based solutions, RAG architectures, fine-tuned models, and embedding-based retrieval systems.
- Architect end-to-end MLOps pipelines covering feature engineering, training, evaluation, deployment, monitoring, and retraining.
- Establish ML CI/CD standards, automated quality gates, model governance checks, reliability, observability, and regulatory compliance.
- Translate commercial business needs into AI/ML solutions for sales forecasting, HCP engagement, customer segmentation, and anomaly detection.
- Partner with analytics, data engineering, and product teams to embed AI capabilities into commercial workflows.
- Communicate technical trade-offs to technical peers and senior business leaders and represent the team in enterprise AI forums.
- Coach engineers, lead design reviews and architecture discussions, and contribute to internal playbooks and reusable AI frameworks.
Requirements
- Master's degree in Computer Science, Machine Learning, Data Science, Statistics, or a quantitative field, or a bachelor's degree with 6+ years of relevant experience.
- 5+ years of experience designing, engineering, and deploying ML/AI systems in production cloud environments.
- Proficiency in Python and strong command of PySpark and SQL.
- Experience defining enterprise-scale AI/ML architecture standards and governance.
- Production MLOps experience including ML CI/CD, MLflow or equivalent model registries, monitoring, and drift detection.
- Experience with AWS, Azure, and/or Databricks.
- Production or near-production experience with LLMs, generative AI, and RAG architectures.
- Preferred experience with multi-agent AI frameworks, autonomous orchestration, and agentic workflow design.
- Preferred hands-on experience with model distillation, fine-tuning, embedding-based retrieval, Hugging Face, LangChain, and vector databases.
- Preferred experience with commercial AI use cases such as next-best-action, HCP targeting, churn prediction, or marketing mix modeling.
- Preferred deep experience with Databricks Unity Catalog, Delta Lake, Feature Store, and Model Serving.
- Preferred experience with Docker, Kubernetes, and Ray.
- Pharmaceutical, life sciences, or healthcare commercial technology experience and relevant AWS, Azure, or Databricks certifications are preferred.
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