REQUIREMENTS:

- Total experience 11 years.
- Minimum 9 years in data science ML engineering with at least 5 years leading ML solutions from design to production.
- Should have experience in software engineering with strong depth in Python.
- Predictive modelling: regression-based estimation with explainable drivers prediction intervals and live what-if re-scoring.
- Similarity and retrieval: hybrid vector and structured-attribute search.
- Hierarchical analytics: scoring sizing and ranking opportunities across multi-level hierarchies.
- Should have exposure to a conversational AI assistant with tool calling grounded and cited answers and guardrails.
- Must have production experience with regression and tabular ML: feature engineering model selection calibration uncertainty (conformal or quantile intervals) and explainability (SHAP coefficients).
- Should have hands-on experience with embeddings vector search and hybrid retrieval and with LLM applications: tool calling retrieval-grounded generation citations guardrails and evaluation.
- Should have hands-on experience for building conversational agent with multi-turn session memory and tool calling into the multiple engines and the semantic layer.
- Should have experience on a major cloud: pipelines model registry drift monitoring retraining and low-latency serving. Experience of Integrating with BigQuery the semantic layer (LookML) and the existing React and backend-for-frontend application.
- Must have hands-on experience with tree-based models (XGBoost or LightGBM) and regression model evaluation and explainability (SHAP).
- Should have practical LLM experience: prompting tool calling retrieval-augmented generation embeddings and vector search.
- Should have experience with ML pipelines and model deployment on a major cloud and with Git testing and code review.
- Should have strong statistics and metrics knowledge including error metrics.
- Must have google cloud experience (Vertex AI BigQuery Gemini Vector Search)



or an equivalent cloud (AWS/Azure).
- Should have experience with agent frameworks prompt-injection testing and LLM evaluation methods.
- Should have experience with semantic layers such as Looker and with enterprise AI governance.
- Should have exposure to cost pricing or supply-chain analytics.
- Must have ability to translate ambiguous business problems into durable technical architectures.
- Should have excellent communication skills with the ability to influence senior stakeholders and technical leadership.

RESPONSIBILITIES:

- Understanding the clients business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
- Set modelling standards: how models are compared and chosen validation and holdout rules explainability uncertainty and acceptance criteria.
- Lead feature engineering and model selection and hands-on build of the hardest models and retrieval pipelines.
- Design the GenAI agent: tool calling retrieval-grounded generation session memory guardrails human approval for actions and layered prompt-injection protection.
- Define evaluation: golden test sets groundedness tool-routing accuracy and release gates and run regular quality reviews.
- Define serving and MLOps practice with the architect and DevOps: model registry pipelines drift monitoring retraining and rollback.
- Apply Responsible AI practice: bias checks auditability conversation logging and retention and explicit explanations for business users.




- Estimate work track risks and dependencies and report status to the delivery lead. Present technical results to business and technical audiences
- Partner with product design data and business leaders to translate strategic objectives into scalable technical solutions.
- Review critical designs and codebases unblock teams on complex technical challenges and raise the overall engineering bar.
- Lead technical discovery and solutioning for high-impact initiatives including client or executive-facing workshops when required.
- Ensure enterprise readiness: security privacy compliance governance and responsible AI practices.
- Mapping decisions with requirements and be able to translate the same to developers.
- Identifying different solutions and being able to narrow down the best option that meets the clients requirements.
- Defining guidelines and benchmarks for NFR considerations during project implementation
- Writing and reviewing design document explaining overall architecture framework and high-level design of the application for the developers
- Reviewing architecture and design on various aspects like extensibility scalability security design patterns user experience NFRs etc. and ensure that all relevant best practices are followed.
- Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies patterns and frameworks to materialize it
- Understanding and relating technology integration scenarios and applying these learnings in projects
- Resolving issues that are raised during code/review through exhaustive systematic analysis of the root cause and being able to justify the decision taken.
- Carrying out POCs to make sure that suggested design/technologies meet the requirements.

Qualifications :

Bachelors or masters degree in computer science Information Technology or a related field.

Remote Work :

No

Employment Type :

Full-time

📌 Principal Engineer, Data Science (Hyderabad)
🏢 Nagarro
📍 Hyderabad