Associate Principal Engineer (AI Solution Architect)

Nagarro; Pune District, Bengaluru, Delhi; Hybrid; INR 1,200,000 - 2,000,000; Full time

Job summary

Nagarro is seeking a seasoned GenAI/Agentic AI Architect to design and run complex AI workflows within enterprise systems. You will translate business problems into durable architectures, embed AI in microservices, and ensure security, governance, and cost controls across cloud environments.

You will lead the creation of reusable components, SDKs, and best practices for prompt design, model integration, evaluation, and observability, while mentoring engineers and collaborating with product

Qualifications

• Total experience 10+ 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 have experience with data and feature preparation; cleaning data and choosing the inputs a model learns from.
• Should have experience in software engineering, with strong depth in Python.
• Must have experience to develop microservices and APIs (Python, FastAPI, Node.js) to embed AI and deep learning models into enterprise applications.
• Must have experience with GenAI and RAG design Document chunking, retrieval and ranking, vector databases (Pinecone, Weaviate, Milvus), LLM selection, prompting
• Should be able to architect and implement scalable ML, Generative AI, and Agentic AI solutions (LLMs, RAG, autonomous agents) aligned with business objectives.
• Should have exposure to building with coding agents, spec-first prompting, generated tests, careful code review
• Should have experience in Agent design, choosing between fixed workflows and autonomous agents, tool design (e.g., MCP), context and memory design, multi-agent patterns. Agent control and safety permissions, human approval for risky actions, limits on steps and spend, failure handling, audit trails
• Should have exposure to AI evaluation; Eval sets, Test sets, task-level agent testing, regression testing, measuring accuracy, speed and cost
• Should be able to build robust end-to-end AI pipelines and integrate Vector databases (e.g., Pinecone, Weaviate, Milvus) for data processing and feature extraction.
• Should be able to leverage emerging AI research to continuously fine tune models, optimize codebases, and manage scalable compute resources.
• Must have experience to deploy secure, production-grade hybrid and cloud AI solutions (AWS/GCP/Azure) using modern CI/CD, AI-assisted code optimization, tracing, cost tracking, versioning, rollback and strict access control.
• Should have strong technical communication to clearly design documents, plain-language explanations for non-technical audiences
• Should be able to mentor junior engineers, drive agile workflows (Jira), and partner with Product managers to define technical strategy.
• Should have strong grasp of AI Governance & Responsible AI, Multi-cloud AI platform architecture, AgentOps / GenAIOps, Knowledge Graphs & Semantic Layer Architecture.
• Should have excellent communication skills, with the ability to influence senior stakeholders and technical leadership.

Responsibilities

• 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 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.

Skills

GenAI

Agentic AI

Python

FastAPI

Node.js

LLMs

RAG

Vector databases

Pinecone

Weaviate

Milvus

Embeddings

Deep learning

Tools

Jira

CI/CD

Job description

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 10+ 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 have experience with data and feature preparation; cleaning data and choosing the inputs a model learns from.


• Should have experience in software engineering, with strong depth in Python.


• Must have experience to develop microservices and APIs (Python, FastAPI, Node.js) to embed AI and deep learning models into enterprise applications.


• Must have experience with GenAI and RAG design Document chunking, retrieval and ranking, vector databases (Pinecone, Weaviate, Milvus), LLM selection, prompting


• Should be able to architect and implement scalable ML, Generative AI, and Agentic AI solutions (LLMs, RAG, autonomous agents) aligned with business objectives.


• Should have exposure to building with coding agents, spec-first prompting, generated tests, careful code review


• Should have experience in Agent design, choosing between fixed workflows and autonomous agents, tool design (e.g., MCP), context and memory design, multi-agent patterns. Agent control and safety permissions, human approval for risky actions, limits on steps and spend, failure handling, audit trails


• Should have exposure to AI evaluation; Eval sets, Test sets, task-level agent testing, regression testing, measuring accuracy, speed and cost


• Should be able to build robust end-to-end AI pipelines and integrate Vector databases (e.g., Pinecone, Weaviate, Milvus) for data processing and feature extraction.


• Should be able to leverage emerging AI research to continuously fine tune models, optimize codebases, and manage scalable compute resources.


• Must have experience to deploy secure, production-grade hybrid and cloud AI solutions (AWS/GCP/Azure) using modern CI/CD, AI-assisted code optimization, tracing, cost tracking, versioning, rollback and strict access control.


• Should have strong technical communication to clearly design documents, plain-language explanations for non-technical audiences


• Should be able to mentor junior engineers, drive agile workflows (Jira), and partner with Product managers to define technical strategy.


• Should have strong grasp of AI Governance & Responsible AI, Multi-cloud AI platform architecture, AgentOps / GenAIOps, Knowledge Graphs & Semantic Layer Architecture.


• Should have excellent communication skills, with the ability to influence senior stakeholders and technical leadership.

RESPONSIBILITIES:

• 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 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.