Principal Engineer - Artificial Intelligence and Machine Learning

Nagarro Softwares Pvt. Ltd; Gurugram District; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Nagarro is seeking a Principal Engineer for Agentic AI to lead the design and delivery of production-scale Generative AI platforms. You will architect end-to-end AI/ML systems, drive multi-team collaboration, and establish best practices for secure, scalable solutions across backend, frontend, and AI infra.

You will work on integration patterns for LLMs, RAG pipelines, and multi-agent orchestration, leveraging tools like MLflow, Vertex AI, Kubeflow, and cloud platforms such as GCP.

Qualifications

• Total experience 11+ years.
• Strong expertise in Python, ML/DL, and statistical modeling.
• Experience designing scalable ML systems and production pipelines.

Responsibilities

• Design end-to-end AI/ML architectures for scalable, production-ready systems.
• Lead GenAI workflows combining LLMs, tools, APIs, data, and user context.
• Establish engineering standards for prompt design, model integration, evaluation and observability.
• Drive GenAI platformisation with reusable components, SDKs, and frameworks.
• Partner with product, design and business leaders to translate strategy into scalable tech solutions.
• Review designs and code, unblock teams on complex challenges, raise engineering bar.
• Ensure security, privacy, governance, and responsible AI practices.

Skills

Python

Machine Learning

Deep Learning

LLM integration

MLOps

CI/CD

System design

Microservices

APIs

GCP Vertex AI

Databricks

LangGraph

CrewAI

AutoGen

Security & governance

Education

Bachelor’s or Master’s degree in Computer Science/Information Technology

Tools

MLflow

Vertex AI

Kubeflow

Job description

Principal Engineer, Agentic AI Company Description We're Nagarro.

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (18000+ experts across 40 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in!

Job Description REQUIREMENTS:

• Total experience 11+ years.
• Should have experience in software engineering, with strong depth in Python.
• Strong expertise in Machine Learning, Deep Learning, and statistical modeling
• Experience designing scalable ML systems and production pipelines
• Hands‑on experience with agentic frameworks (LangGraph, CrewAI, AutoGen) and multi‑agent orchestration
• Should have proven experience architecting and delivering production‑grade Generative AI applications at scale.
• Should have deep understanding of LLM integration patterns, RAG systems, and AI‑driven UX design.
• Should have strong system design skills across backend, frontend, and AI infrastructure layers.
• Must have experience defining technical strategy and influencing architecture across teams or pods.
• Must have experience with microservices, APIs, and scalable backend systems.
• Strong experience in MLOps practices including CI/CD, model versioning, monitoring, and governance.
• Hands‑on experience with tools such as MLflow, Vertex AI, Kubeflow, or similar.
• Should have deep experience with cloud platforms, especially GCP (Vertex AI, BigQuery) and/or Databricks
• Should have strong grasp of security, privacy, and governance considerations for enterprise AI.
• 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.
• Design end‑to‑end AI/ML architectures, including scalable, secure, and production‑ready systems using Machine Learning, Deep Learning, and Large Language Models (LLMs).
• Establish best practices for building robust and reusable AI platforms.
• Design scalable, secure, and cost‑efficient backend platforms for LLM inference, RAG pipelines, and agent‑based orchestration.
• 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 platformisation—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.
• Use AI‑assisted development tools (e.g., Copilot, Claude Code) to accelerate delivery while maintaining production‑grade quality.
• 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.

Qualifications

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