AI/ML

This AI/ML role in Infosys Limited’s Data & Analytics Unit is based in Bangalore. The position leads end-to-end AI/ML initiatives, turning business goals into model strategies, delivery milestones and measurable success metrics.

Responsibilities

• Direct model selection, training, evaluation and deployment approaches for production-grade ML systems. Build and optimize models using structured and unstructured data, with attention to robustness, generalization and interpretability where needed.
• Design NLP pipelines for product needs such as text classification, entity extraction, semantic search, summarization and intent detection. Work with data stakeholders on data quality checks, feature engineering, labeling strategies and feedback loops.
• Establish evaluation practices covering offline metrics, error analysis, bias checks and A/B testing where applicable. Set standards for reproducibility, experiment tracking, documentation and model governance.
• Mentor ML engineers and data scientists through design and code reviews and model-performance discussions. Partner with product and engineering teams on user experience, latency, scalability and reliability; explain trade-offs and results to technical and non-technical stakeholders. Identify risks such as data drift, model decay and dependency gaps, and plan mitigations.

Required qualifications

• 5–9 years of overall experience, including hands-on ownership of real-world AI/ML solution delivery. Strong capabilities in model development, training, evaluation and iterative improvement, plus experience in NLP, data learning workflows, feature engineering and experimentation.
• Ability to lead technical discussions, mentor colleagues and coordinate multiple workstreams. The posting lists Bachelor of Engineering, BTech, MTech, MSc and MCA educational requirements, and specifies BTech, MTech, MCA, MSc or equivalent in its minimum qualifications.

Preferred qualifications and skills:

• Experience leading production ML deployments, including monitoring, retraining and ongoing performance optimization; knowledge of transformer-based NLP, embeddings and prompt-based workflows and their evaluation.
• Experience designing scalable ML architectures with platform teams, establishing experimentation, versioning and governance practices, and delivering measurable business impact. Good-to-have skills include MLOps, model monitoring and drift detection, feature stores, A/B testing and data engineering pipelines.