Senior AI Engineer

About the Role
IndusInd Bank's AI Centre of Excellence is looking for a highly skilled Senior AI Engineer to design, develop, and deploy next-generation AI/ML and Generative AI solutions. The role demands strong hands-on programming expertise, experience in building production-ready AI systems, and the ability to independently deliver scalable enterprise-grade solutions while mentoring junior team members.
Key Responsibilities
Design, build, and deploy AI/ML models and Generative AI applications using Python on the Databricks platform.
Develop and implement LLM-powered solutions, including prompt engineering, Retrieval-Augmented Generation (RAG), embeddings-based workflows, and API-driven model inference.
Write high-quality, production-grade code for data preprocessing, feature engineering, model training, evaluation, and deployment.
Build and maintain scalable data pipelines using Azure Data Lake Storage (ADLS Gen2), Delta Lake, and Delta Live Tables.
Operationalize AI solutions through MLflow, model serving, monitoring frameworks, and CI/CD pipelines.
Develop AI agents, multi-agent systems, guardrails, and traceability mechanisms for enterprise AI use cases.
Collaborate closely with Data Scientists, Product Managers, Data Engineers, and AIOps teams to deliver impactful business solutions.
Conduct code reviews, provide technical guidance, and mentor junior AI Engineers to support capability development within the team.
Required Qualifications & Skills
2 to 5 years of hands-on experience in AI/ML Engineering, Software Development, Data Science, or Generative AI solution development.
Strong proficiency in Python and AI/ML libraries, including NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow.
Solid understanding of Machine Learning concepts, including supervised and unsupervised learning, feature engineering, model evaluation, and deployment.
Practical experience with Large Language Models (LLMs), prompt engineering, embeddings, vector search, RAG architectures, and API-based model integration.
Hands-on experience with Databricks, including notebooks, workflows, MLflow, model serving, and deployment practices.
Experience working with Azure cloud services, particularly ADLS Gen2 and related data engineering components.
Strong knowledge of SQL, REST APIs, Git, and software engineering best practices.
Understanding of MLOps and LLMOps concepts, including model registry, monitoring, staged deployments, retraining workflows, and governance.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
Preferred Qualifications
Experience with Agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or Databricks Agent Framework.
Prior experience in the Banking, Financial Services, or FinTech domain.
Familiarity with Responsible AI practices, model governance, guardrails, explainability, monitoring, and auditability requirements.
Exposure to enterprise-scale AI deployments and cloud-native architectures.

Work arrangement
No