Software Engineer

At IBM Finance & Operations, we are the backbone of IBM’s transformation driving efficiency, transparency, and smart decision-making across the business. Our teams provide the insight and discipline that guide strategy, ensure financial strength, and enable IBM to invest in innovation and growth. Working in Finance & Operations means combining analytical skills with collaboration and curiosity. You’ll partner with colleagues across functions and geographies, using data, technology, and process excellence to create solutions that improve performance and deliver measurable impact. IBM offers continuous learning, career development, and a culture that values diverse perspectives. Join us and be part of a global team that keeps IBM moving forward, while building your own future in a dynamic and evolving environment. The Software Engineer exercises good judgment and is responsible for working independently with minimal instruction to prioritize work and resolve moderately complex issues. This role builds, optimizes, and scales machine learning models while contributing to innovative AI-driven solutions, and assisting users in understanding ML predictions. Collaboration with cross-functional teams ensures successful project outcomes.    Model Development and Training: Ability to manipulate (i.e. optimize) model training and model serving frameworks. Feature Engineering: Engineer features from raw data, including unstructured formats, to enhance model effectiveness. Algorithm Selection: Experiment with algorithms to select the most effective ones for project objectives. Model Evaluation: Use advanced metrics to validate model performance and improve reliability. Model Inference: Algorithmic and System level optimizations for inference. Building reliable production model serving platform supporting a variety of accelerators. Mentorship: Lead technical design discussions and training to other engineers Troubleshoot complex production issues involving ML Model Development and Training: Ability to manipulate (i.e. optimize) model training and model serving frameworks. Feature Engineering: Engineer features from raw data, including unstructured formats, to enhance model effectiveness. Algorithm Selection: Experiment with algorithms to select the most effective ones for project objectives. Model Evaluation: Use advanced metrics to validate model performance and improve reliability. Model Inference: Algorithmic and System level optimizations for inference. Building reliable production model serving platform supporting a variety of accelerators. Mentorship: Lead technical design discussions and training to other engineers Troubleshoot complex production issues involving ML India Software Engineering Hybrid Professional Bangalore, IN (0063) IBM India Private Limited