EY is hiring a GenAI Developer with 2โ6 years of experience for opportunities in Bengaluru and Kolkata. This role is designed for engineers who can build production-grade Generative AI and multi-agent systems, rather than candidates whose experience is limited to creating basic RAG applications.
๐ข About EY
EY is a global professional-services organization providing services across Assurance, Consulting, Strategy and Transactions, and Tax. EY’s technology organization works with clients on areas including AI, cloud, data, cybersecurity, software engineering, and digital transformation. EY India describes its technology careers as opportunities to solve complex business challenges using advanced technologies.
AI is an increasingly important part of EY’s technology strategy. EY says its internal EYQ generative-AI tool supports employees across use cases, while its AI learning programs include Responsible AI, Applied AI, and AI Engineering.
๐ Job Details
๐น Company: EY
๐น Position: GenAI Developer
๐น Experience: 2โ6 Years
๐น Locations: Bengaluru / Kolkata
๐น Preference: Immediate Joiners
๐น Primary Focus: Generative AI + Multi-Agent Systems
๐น Frameworks: LangGraph / LangChain / Microsoft Agent SDK / Google ADK
๐น LLM: Validation + Fine-tuning
๐น Cloud AI: Azure AI Services / GCP Document AI
๐น Specialization: Production-grade GenAI solutioning
๐น ๐ง Email: diksha.bellani@in.ey.com
๐ Why This Role Is Good for Your Experience
This is an excellent opportunity for GenAI engineers who want to move from experimentation into production AI engineering. The role requires designing multi-agent workflows capable of handling natural-language requests across structured and unstructured data, performing image analysis, and generating complete business outputs such as KPIs, analysis, inferences, and presentations.
That means you can potentially gain experience across the entire AI application lifecycle: problem understanding โ agent orchestration โ tool/API integration โ model validation โ optimization โ content generation โ deployment โ production support.
The emphasis on cost-optimized API orchestration is another major advantage. Companies increasingly need AI systems that are not only accurate but also commercially viable. Learning how to balance model quality, latency, token usage, API selection, caching, routing, and reliability can significantly strengthen your GenAI engineering profile.
๐งฐ Skills Required
๐น Python programming and production software engineering
๐น LangGraph and LangChain
๐น Microsoft Agent SDK or Google ADK
๐น Multi-agent architecture and orchestration
๐น Tool/function calling
๐น Prompt engineering and structured outputs
๐น LLM evaluation and validation
๐น Expert-level LLM fine-tuning
๐น Model selection and API orchestration
๐น Token/cost optimization
๐น Azure AI Services
๐น Azure Document Intelligence
๐น GCP Document AI
๐น Image/document understanding
๐น Structured and unstructured data processing
๐น REST APIs and JSON
๐น Async workflows
๐น Docker and CI/CD fundamentals
๐น AI observability and monitoring
๐น Responsible AI and security
๐น Computer Vision โ advantageous
๐น Presentation/content-generation automation
๐ฏ Expected EY Interview Rounds
Round 1 โ Recruiter Screening: Experience, current project, GenAI stack, notice period, location, compensation, and availability.
Round 2 โ GenAI Technical Round: Python, LLM concepts, LangChain/LangGraph, agents, tool calling, prompt engineering, model evaluation, fine-tuning, and API integration.
Round 3 โ Architecture/Hands-on Round: You may be asked to design a multi-agent system, explain agent communication, tool orchestration, failure handling, memory, observability, security, and production deployment.
Round 4 โ Managerial/Client Discussion: Expect project ownership, stakeholder communication, business problem-solving, consulting mindset, and explaining complex AI concepts to non-technical stakeholders.
Round 5 โ HR/Final: Compensation, joining date, location, career objectives, and other employment details.
๐ฐ Expected Salary Range
For a 2โ6-year GenAI Developer in Bengaluru/Kolkata with strong agentic-AI expertise, a reasonable indicative market expectation is โน12โ25+ LPA, depending on experience, current CTC, depth of production GenAI work, framework expertise, cloud skills, and internal EY level. Exceptional candidates with substantial production-grade multi-agent experience may command higher compensation. This is a market estimate, not an official EY salary range.
๐ง Preparation Tips
๐ค Build a real multi-agent system: Don’t prepare only RAG. Build an architecture with specialized agents such as Planner, Data Analyst, Document Analyst, and Presentation Generator.
๐ Go deep into LangGraph: Understand state management, nodes, edges, conditional routing, checkpoints, retries, human-in-the-loop workflows, and agent control.
๐งช Prepare LLM evaluation: Know how to evaluate accuracy, groundedness, relevance, consistency, hallucination, latency, and cost.
๐ฐ Practice cost optimization: Be ready to explain model routing, token reduction, caching, batching, prompt optimization, and selecting different models according to task complexity.
๐ Learn Document Intelligence: Practice extracting tables, fields, text, and structured information from PDFs/images and feeding the results into downstream agents.
๐ผ๏ธ Prepare image-analysis scenarios: Understand OCR, document classification, visual extraction, multimodal LLMs, and validation of image-derived information.
๐ Practice end-to-end output generation: Design a workflow that takes a natural-language business request, analyzes data, calculates KPIs, generates insights, and creates a presentation in a specified template.
๐ Resume Tips
Your resume should immediately communicate Agentic AI + Production GenAI, not just โRAG developer.โ
โ
Put LangGraph/LangChain and your agent frameworks prominently near the top.
โ
Describe multi-agent architectures you actually built.
โ
Quantify improvements in latency, cost, accuracy, throughput, or automation.
โ
Mention LLM evaluation and validation frameworks.
โ
Highlight fine-tuning work with the model, dataset, approach, and outcome.
โ
Add Azure AI Services or GCP Document AI experience.
โ
Include multimodal/image-analysis projects if applicable.
โ
Mention API orchestration and third-party integrations.
โ
Show production deployment, monitoring, CI/CD, and troubleshooting experience.
๐ฉ How to Apply
๐ง Email: diksha.bellani@in.ey.com
๐ Locations: Bengaluru / Kolkata
โณ Experience: 2โ6 Years
โก Preferred: Immediate Joiners
Important: The recruiter has specifically requested that candidates include all of the following details in the email because follow-up for missing information may not be possible:
๐น Position applied for + years of experience
๐น Current CTC
๐น Expected CTC
๐น Immediate Joiner: Yes/No
๐น If No, mention your notice period
๐ Final Takeaway
This EY opportunity is aimed at GenAI engineers who can build and operate sophisticated AI systems, not simply connect an LLM to a vector database. The strongest candidates will demonstrate hands-on expertise in multi-agent orchestration, LangGraph/LangChain or equivalent agent frameworks, LLM validation, fine-tuning, cost optimization, cloud AI services, multimodal processing, and production deployment.
