Associate Principal Engineer (Data Scientist - Gen AI, ML) Nagarro

Key responsibilities include create integration architectures, define standards that matches emerging GenAI trends to drive innovation, efficiency and competitive advantage, integrate AI/LLM Models into Applications/APIs, optimize workflows with GenAI tools, leading technical discussions and mentoring junior team members.

The role also requires experience with backend development, APIs including RAG APIs, Vector databases, cloud platform, production grade solutions code quality & security.

Regular duties:

• Design solutions and implement ML, generative AI and agentic AI models and algorithms, utilizing state-of-the-art algorithms.
• Work on integrating cloud with AI algorithms and On premises AI algorithms.
• Collaborate with cross-functional teams to define AI project requirements and objectives, ensuring alignment with overall business goals.
• Research the latest advancements in generative AI, machine learning, and deep learning techniques, and identify opportunities to integrate them into our products and services.
• Optimize existing AI models for improved performance, scalability, and efficiency.
• Develop and maintain AI pipelines, including data pre-processing, feature extraction, model training, and evaluation.
• Drive the team to build AI products and solutions, working in an agile methodology
• Manage delivery across pods building scalable AI and API services
• Run standups, sprints, retrospectives, and Jira workflows
• Drive backend deployment, security reviews, and QA pipelines
• Coordinate integration of RAG APIs and vector DB pipelines
• Promote use of code quality agents and modern CI/CD practices
• Collaborate with the Product Manager/Architect to define deliverables and technical strategy.
• Drive the integration of AI/ML capabilities into products using market trending libraries.
• Guide teams through refactoring and codebase optimization using AI-assisted insights.
• Troubleshoot and resolve issues related to AI models and implementations.
• Create and maintain documentation for AI models and their applications.
• Communicate complex technical concepts and findings to non-technical stakeholders.