Research Software Engineer

About IBM IBM India since 1992 provides solutions and services spanning all major industries including financial services, healthcare, government, automotive, telecommunications and education, among others. As a trusted partner with wide-ranging service capabilities, IBM helps clients transform and succeed in challenging circumstances. The diversity and breadth of the entire IBM portfolio of research, consulting, solutions, services, systems and software, uniquely distinguishes IBM India from other companies in the industry. Business Unit Introduction IBM Software Innovation Lab is the global IBM organization that executes the research agenda focused on next-generation data systems, agents and automation, and drives these innovations at scale into IBM's Software product portfolio. IBM Software Innovation Lab – India (https://ibm.biz/ibmsilindia), as part of this global organization, works on breakthrough research in these areas driven by real-world market needs and enterprise scale data, and collaborates closely with the product engineering teams to drive integration of this research into IBM’s global products to create market leading technologies. We are looking for a talented and highly motivated engineer to contribute to our research and development efforts across multiple frontiers of AI. The candidate will work on innovation projects that are grounded in real-world datasets, environments and problems while also pushing the state of the are in agentic AI technology with a specific focus on enterprise applications. Areas of research include AI for IT automation, intersection of data and AI, agentic middleware and models, embedding and AI-based retrieval, time series foundation models, multi-modal foundation models and AI for software engineering. Excellent coding skills in Python (including Pandas, NumPy) Strong grasp of Data Structures, Algorithms, Problem Solving Solid foundation in Linear Algebra, Probability, and Statistics Hands-on experience with VS Code, Jupyter Notebooks, Git AI/ML Fundamentals: supervised & unsupervised learning, classification, regression, neural networks, clustering Learning fundamentals and Data Manipulation/Analysis for large datasets Proficiency in AI libraries/frameworks: TensorFlow, PyTorch Experience in Deep Learning, Foundation Models, Large Language Models Familiar with Generative AI frameworks such as langchain and crew.ai Expertise in unstructured data processing. Experience working with platforms such as Watsonx.ai or similar Familiarity with risks associated with AI deployments, including agents going rogue.