Principal Engineer, Data Science
Nagarro (company site, opens in a new tab)11+ yrsHyderabadOn-siteDirect career pageEst. 30–48 LPA
Posted 21h ago
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Job description
Structured overview of role & requirements
About This Role
- Own end-to-end design and production deployment of complex ML solutions including predictive models, retrieval pipelines, and conversational AI agents with multi-turn session memory and tool calling.
- Lead the definition and enforcement of modelling standards, evaluation criteria, MLOps practices (model registry, drift monitoring, retraining) and Responsible AI governance across projects.
- Partner with cross-functional teams and senior stakeholders to translate ambiguous business problems into scalable, secure, compliant technical architectures and solutions.
Minimum Requirements
- Total experience: Minimum 11+ years, with at least 9+ years in data science and ML engineering and 5+ years leading ML solutions from design to production.
- Strong Python software engineering skills and hands-on experience with regression and tree-based models (XGBoost or LightGBM), feature engineering, model evaluation, explainability (SHAP), and uncertainty estimation.
- Experience with conversational AI, LLM applications including prompting, embedding, vector search, tool calling, retrieval-grounded generation, and multi-turn session memory.
- Experience with a major cloud platform (Google Cloud preferred - Vertex AI, BigQuery, Gemini, Vector Search) including ML pipelines, model registry, drift monitoring, retraining, low-latency serving; integration with BigQuery, semantic layers (LookML/Looker), and deployment best practices.
Ideal Candidate Profile
- Senior technical leader comfortable leading complex ML and AI solution architecture, hands-on coding, and cross-team collaboration in cloud environments.
- Expertise in advanced predictive modeling, LLM-based conversational AI, and retrieval systems combined with practical MLOps and Responsible AI implementation.
- Experience bridging business and technical domains to solve ambiguous problems with robust, scalable, and enterprise-ready AI architectures and governance.