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 clients 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 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.

Work from home :

No

Employment Type :

Full-time

📌 Principal Engineer, Data Science AI Architect (Gurugram)
🏢 Nagarro
📍 Gurugram