Principal Engineer, Data Science

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 client’s 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 clear 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 client’s 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.

Department: Engineering
Function: Information Technology
Experience Level: Not Applicable