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At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless workbut its work worth doing. If youre driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our 39,000 employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease. We're looking for people who are determined to make life better for people around the world. The Lilly Bengaluru Business Insights & Analytics team was started in 2017 with the objective of using innovative data mining and analytics to support business decisions to Marketing functions in the US and ex-US affiliates (focused on in-line and pre-launch brands). This team has rapidly grown and currently comprises of more than 100 staff members, with varied backgrounds and skills across data management, data sciences, analytical techniques, pharmaceutical commercial operations, and business insights. The team provides analytics outcomes for driving decision making across Lilly's Marketing, Sales, Medical Affairs, and a range of other functions. To support the marketing teams in their decision-making, a data and analytics team has been set up simultaneously in Indianapolis (HQ) and Bengaluru (Lilly Bengaluru). This team is responsible for setting up the data warehouses necessary to handle large volumes of digital streaming data, create meaningful analyses using that data, and deliver recommendations to leadership. As part of the Lilly Bengaluru team, we have an exciting opportunity for a Senior ML/AI Engineer who will own complex ML architecture and pipeline decisions across key initiatives while also leading technical integration with Lilly's Agentic AI engineering team as our programs adopt agentic AI capabilities. This is a hands-on, individual contributor role that calls for genuine depth on both sides: production-grade ML engineering and agentic AI development. Core Responsibilities: Own end-to-end ML architecture, feature engineering, and pipeline design decisions for key commercial analytics initiativesDesign and review ML architectural decisions with stakeholders, setting patterns that other engineers build onOwn CI/CD pipeline orchestration, deployment (Docker/Kubernetes/Prefect), and production monitoring across multiple projectsApply software engineering rigor and best practices to ML systems, including CI/CD, automation, and testingOptimize model hyperparameters and evaluate model performance, robustness, and explainability across production ML systemsDesign and build production agentic AI systems using frameworks such as LangGraph multi-step reasoning, tool use, and orchestration across complex workflowsOwn the LLMOps practice for initiatives you lead: prompt versioning, evaluation pipelines, cost/latency monitoring, and guardrails for production LLM applications (Claude or similar)Architect retrieval-augmented generation systems and integrate vector databases (e.g., Pinecone) for semantic search and retrieval at production scaleServe as the primary technical point of contact with Lilly's Agentic AI engineering team, defining technical contracts, APIs, and shared SLAs as programs adopt agentic capabilitiesSet and document human-in-the-loop boundaries in partnership with Data Science and business stakeholdersProvide informal technical oversight for 2-3 more junior engineers reviewing designs and code, and unblocking hard technical problems, without formal people-management responsibilityCoordinate with diverse stakeholders such as Data Scientists, software engineers, and infrastructure teams to design the most optimal ML and agentic pipelinesRequired 13+ years of demonstrated expertise building ML/AI systems in production including model versioning, data/model lineage, monitoring, deployment, optimization, scalability, and automated pipelines with substantial recent depth in generative AI and agentic system development, not just brief exposureKnowledge of architectural design and implementation of end-to-end ML and agentic AI solutionsStrong knowledge of core ML frameworks (scikit-learn, PyTorch, TensorFlow, Keras, or equivalent) and the ability to understand and extend the modeling work of Data Scientists into production-grade systemsStrong knowledge of Python and PySpark for large-scale data processing; working knowledge of .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.