AI Engineer, BI and A Data Science and AI
Eli Lilly and Company
Bengaluru, Karnataka, IndiaMID
AIHealthcareEngineering
Job Description
You will design and build the platforms, pipelines, and applications that take machine learning and AI solutions from prototype to production at scale.
Responsibilities
- This role sits at the intersection of software engineering, MLOps, and applied generative AI. You will design and build the platforms, pipelines, and applications that take machine learning and AI solutions from prototype to production at scale. This role is well suited for someone who is comfortable operating independently, contributing to technical design decisions, and building reliable production‑grade systems.
- Design, build, and maintain CI/CD pipelines using GitHub Actions for ML and AI application deployments
- Contribute to deployment architecture and infrastructure design decisions for new ML/AI projects
- Deploy and orchestrate ML/AI workloads across environments using Docker, Kubernetes, and Prefect
- Apply and champion software engineering best practices including version control, code review, testing, and documentation across ML/AI systems
- Develop and deploy production‑grade applications using Claude or similar large language models
- Build agentic AI systems using frameworks such as LangGraph, including tool use, multi‑step reasoning, and retrieval‑augmented generation architectures
- Contribute to LLMOps practices including prompt versioning, evaluation pipelines, cost and latency monitoring, and guardrails
- Help define scalable implementation patterns for LLM‑powered applications and retrieval systems
- Integrate vector databases such as Pinecone or similar platforms for semantic search and knowledge retrieval
- Own operational aspects of the model lifecycle, including deployment, monitoring, retraining, and decommissioning
- Monitor production models for data drift, model drift, and performance degradation, and drive issue triage and resolution
- Contribute to and extend team MLOps frameworks to support new models and use cases
- Partner with data scientists, software engineers, infrastructure teams, and business stakeholders to translate requirements into scalable technical solutions
- Contribute to technical design discussions for proof‑of‑concept and production solutions
- Independently drive portions of technical delivery while escalating risks and dependencies appropriately
- Strengthen team standards for AI engineering through hands‑on contribution and continuous improvement
Qualifications
- Python
- Git/GitHub
- Software engineering best practices
- CI/CD fundamentals
- Docker
- Kubernetes
- Prefect
- Production monitoring
- MLOps pipelines
- Model versioning and lineage
- Agentic AI frameworks and design patterns
- LangGraph
- Pinecone or similar vector databases
- Retrieval‑augmented generation architectures
- LLM application development
- Prompt engineering and evaluation
- AWS services such as EC2, ECS, S3, Lambda, IAM, and CloudWatch, or equivalent services
- 5-8 years of hands‑on experience building and operating ML/AI pipelines in production environments
- Strong proficiency in Python, with a track record of writing clean, testable, production‑quality code
- Demonstrated experience with containerization, orchestration, and CI/CD pipelines in production settings
- Working knowledge of AWS cloud services and experience designing and deploying solutions using managed services
- Familiarity with MLOps practices including model versioning, monitoring, automated retraining, and deployment strategies
- Ability to operate independently on well‑scoped problems while collaborating effectively on larger platform and architecture decisions
- Strong verbal and written communication skills, including the ability to explain technical decisions to both technical and business audiences
Nice to have
- Experience with Streamlit
- Knowledge of cloud infrastructure
Benefits
- Work remotely
- Great benefits package