Senior Software Engineer - AI/ML at Bristol Myers Squibb in TS, IN

Job description

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it. Role Overview The Senior Software Engineer - AI/ML will design, build, and deploy machine learning and generative AI solutions that support key business priorities, partnering closely with stakeholders to define requirements, shape user stories, and deliver production-ready models and applications. Key Responsibilities Collaborate with IT partners, business teams, and the broader IT matrix organization to support solution design and delivery of new digital capabilities, and contribute to the enablement and simplification of existing ones Work with IT partners and business teams to understand requirements and use cases; help align requirement outcomes to support successful delivery of new initiatives and enhancements Design and develop Generative AI solutions leveraging LLMs for enterprise use cases including text generation, summarization, Retrieval-Augmented Generation (RAG), and intelligent assistants Contribute to Agentic AI systems using multi-agent frameworks to help automate workflows and decision-making pipelines Break down AI/ML initiatives into user stories, epics, and sprint-ready tasks using Agile/Scrum methodologies Gather requirements, propose solution options, and communicate technical concepts clearly to non-technical stakeholders Write clean, scalable, production-ready Python code following software engineering best practices Support the design of machine learning systems; research and apply appropriate ML algorithms and tools Assist with data processing, access, storage, governance, security, and operations Help select appropriate datasets and data representation methods for distributed processing and storage Contribute to system architecture and design documentation under the guidance of senior engineers/leads Run machine learning tests and experiments; train and retrain models as needed Perform statistical analysis and fine-tuning based on test results Extend existing ML libraries and frameworks while keeping up with developments in the field Contribute to Proof of Concepts (POCs), factoring in user management, security, data privacy, and performance considerations Help identify high-value capabilities and opportunities for continuous improvement when evaluating platform choices Desired Experience Traditional Machine Learning Experience building and deploying production ML models using Scikit-learn , NumPy , and Pandas Hands-on experience with TensorFlow/Keras for supervised and unsupervised learning tasks Experience with feature engineering, model evaluation, and managing the ML lifecycle (e.g., MLflow ) Generative AI Hands-on experience building generative AI applications, including prompt engineering and LLM integration Experience with agent orchestration frameworks such as LangChain , LangGraph or Claude Agent SDK Familiarity with LLM platforms such as Azure OpenAI , AWS Bedrock , or Google Vertex AI Working knowledge of vector databases (e.g., pgVector , AWS OpenSearch ) for retrieval-augmented generation Experience evaluating and monitoring LLM/GenAI application quality using frameworks such as LangSmith Stakeholder Engagement & Solutioning Proven ability to work directly with business stakeholders to gather and define requirements Experience translating requirements into clear, actionable user stories Ability to evaluate and present multiple solution options with tradeoffs to stakeholders and technical leadership Strong communication skills to bridge technical