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 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.

Bachelor’s or master’s degree in computer science, Information Technology, or a related field.