11 hours ago
Hyderābād, IndiaStaff+
Responsibilities
- Translate client business use cases and technical requirements into scalable technical designs and architectures.
- Set standards for model comparison, validation, holdout rules, explainability, uncertainty, and acceptance criteria.
- Lead feature engineering, model selection, and hands-on development of complex predictive models and retrieval pipelines.
- Design conversational GenAI agents with tool calling, retrieval-grounded generation, session memory, guardrails, human approval, and prompt-injection protection.
- Define evaluation frameworks using golden test sets, groundedness, tool-routing accuracy, quality reviews, and release gates.
- Define MLOps and serving practices including model registries, pipelines, drift monitoring, retraining, and rollback.
- Apply responsible AI practices including bias checks, auditability, conversation logging, retention, and business-user explanations.
- Review critical designs and codebases, resolve complex technical issues, conduct POCs, and raise engineering standards.
- Lead technical discovery, solutioning, architecture reviews, and client or executive-facing workshops.
- Partner with product, design, data, business, and engineering leaders to deliver strategic technical solutions.
- Ensure security, privacy, compliance, governance, extensibility, scalability, usability, and other non-functional requirements.
- Estimate work, track risks and dependencies, report status, and communicate technical results to business and technical audiences.
Requirements
- 11+ years of total professional experience.
- 9+ years of experience in data science or ML engineering, including at least 5 years leading ML solutions from design through production.
- Software engineering experience with strong Python skills.
- Production experience with regression and tabular machine learning, including feature engineering, model selection, calibration, uncertainty estimation, explainability, and evaluation.
- Hands-on experience with tree-based models such as XGBoost or LightGBM, regression, SHAP, embeddings, vector search, and hybrid retrieval.
- Practical experience building LLM applications and conversational agents with prompting, tool calling, retrieval-augmented generation, citations, guardrails, evaluation, and multi-turn session memory.
- Experience with major-cloud ML pipelines, model deployment, model registries, drift monitoring, retraining, and low-latency serving.
- Experience integrating BigQuery, LookML semantic layers, React applications, and backend-for-frontend applications.
- Google Cloud experience with Vertex AI, BigQuery, Gemini, and Vector Search, or equivalent AWS or Azure experience.
- Experience with agent frameworks, prompt-injection testing, LLM evaluation methods, semantic layers such as Looker, and enterprise AI governance.
- Strong statistics and metrics knowledge, including error metrics, plus exposure to cost, pricing, or supply-chain analytics.
- Experience with Git, testing, code review, and cloud-based ML operations.
- Ability to translate ambiguous business problems into durable technical architectures and influence senior stakeholders and technical leadership.
- Bachelor's or master's degree in computer science, Information Technology, or a related field.
Categories
About Nagarro
Nagarro provides digital engineering and IT consulting services to enterprises, delivering custom software development, cloud modernization, data and analytics, IoT, and digital commerce solutions. Its business model centers on project-based and managed services across the full product lifecycle, from strategy and design to build and operations. Nagarro is a public company headquartered in Munich and listed on the Frankfurt Stock Exchange, with global delivery across multiple regions.