Principal Engineer, Data Science AI Architect

REQUIREMENTS: Total experience 11+ years. Must have experience to design, build and run GenAI and Agentic AI solutions inside enterprise systems, fitting AI into existing systems, security rules and budgets. Must have ability to translate ambiguous business problems into durable technical architectures. Must have strong ML fundamentals; how models are trained, tested and compared; spotting overfitting and weak data, deep learning basics; embeddings, classification, ranking models Should be able to architect and implement scalable ML, Generative AI, and Agentic AI solutions (LLMs, RAG, autonomous agents) aligned with business objectives. Must have experience in data science, ML engineering or AI architecture, with at least 5 years leading ML solutions from design to production. Should have expertise in Python, SQL and Solid statistics, including outlier detection and error metrics Should have exposure to time series forecasting. Should have production experience with regression and tabular ML: feature engineering, model selection, calibration, uncertainty (conformal or quantile intervals) and explainability (SHAP, coefficients). Must have experience with hierarchical forecasting or reconciliation, and prompt-injection testing. 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. Must have experience on a major cloud: pipelines, model registry, drift monitoring, retraining and low-latency serving. Should have experience with Google Cloud (Vertex AI, BigQuery, Gemini, Vector Search), or equivalent on another cloud. Design retrieval-grounded generation with citations, input and output guardrails, human approval for actions, and layered, attack-tested prompt-injection protection ESPONSIBILITIES: Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements. Lead the design and implementation of complex GenAI workflows that combine LLMs, tools, APIs, structured data, and user context. Establish engineering standards and best practices for prompt design, model integration, evaluation, and observability. Drive GenAI platform building reusable components, SDKs, and frameworks used across multiple teams or products. 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