Zensar Technologies is hiring experienced technology professionals for the position of Generative AI Engineer. This opportunity is aimed at candidates with 2β4 years of experience who have hands-on exposure to Generative AI, Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), Python, AI agent frameworks, and cloud platforms.
π Job Details
π’ Company: Zensar Technologies
πΌ Position: Generative AI Engineer
π¨βπ» Experience: 2β4 Years
β³ Notice Period: Immediate Joiners Only
π§ Core Focus: Generative AI, LLMs, Agentic AI, RAG
π€ Frameworks: LangGraph, CrewAI, AutoGen
π Programming: Python
βοΈ Cloud: AWS / Azure / GCP
π Emerging Technologies: MCP and A2A frameworks
π© Apply: sujata.kadam@zensar.com
π’ About Zensar Technologies
Zensar Technologies is a technology and digital services organization working with businesses across areas such as software engineering, cloud, data, digital transformation, and emerging technologies. Its technology-focused environment makes this Generative AI position relevant for engineers who want to work on practical enterprise AI applications rather than limiting their experience to experimental models.
π Why This Role Is Good for Experience?
πΉ End-to-end AI development: Candidates can work across the lifecycle from experimentation and PoCs to production-ready applications.
πΉ Agentic AI exposure: Building intelligent agents provides experience beyond conventional prompt-and-response applications.
πΉ RAG expertise: Designing systems that retrieve relevant enterprise information before generating responses is highly valuable for real-world AI applications.
πΉ Multi-agent frameworks: Exposure to LangGraph, CrewAI, and AutoGen helps engineers understand different approaches to orchestration and agent collaboration.
πΉ Cloud AI engineering: Working with AWS, Azure, or GCP provides experience deploying and operating AI workloads in cloud environments.
πΉ Emerging protocols: MCP and A2A exposure can help engineers understand newer approaches to connecting AI agents, tools, data, and services.
πΉ Production mindset: The role explicitly includes production-ready solutions, making reliability, observability, security, latency, and cost important engineering considerations.
π οΈ Skills Required
πΉ Python programming and backend development
πΉ LLM fundamentals, prompting, embeddings, tokenization, and model selection
πΉ RAG architecture including document ingestion, chunking, embeddings, retrieval, reranking, and generation
πΉ Agentic AI concepts including tools, memory, planning, workflows, and agent orchestration
πΉ LangGraph, CrewAI, and/or AutoGen hands-on experience
πΉ Vector databases and similarity search concepts
πΉ REST APIs and integration with external services
πΉ Evaluation of LLM and agent responses
πΉ AWS, Azure, or GCP cloud services
πΉ Git, CI/CD, containers, and deployment fundamentals
πΉ Monitoring, logging, security, and responsible AI concepts
πΉ Understanding of MCP and A2A concepts is particularly relevant for this position
π― Expected Interview Rounds at Zensar
πΉ Recruiter Screening: Experience, current role, GenAI background, location, compensation, and immediate-joining availability may be discussed.
πΉ GenAI Technical Round: Expect questions around LLMs, RAG, embeddings, vector databases, prompt engineering, hallucination reduction, and AI application architecture.
πΉ Agentic AI / Framework Round: Be prepared to explain how you have used LangGraph, CrewAI, AutoGen, or similar orchestration approaches and when you would choose one architecture over another.
πΉ Hands-on / System Design Discussion: You may be asked to design an enterprise GenAI application involving retrieval, tools, agents, APIs, cloud infrastructure, security, and monitoring.
πΉ Managerial/Client Round: Communication, ownership, solution design, project experience, trade-offs, and ability to convert PoCs into useful business solutions may be evaluated.
π° Salary Range
The salary for this Generative AI Engineer position is not disclosed in the provided hiring post. Compensation can vary according to total experience, depth of GenAI expertise, cloud knowledge, current CTC, technical interview performance, and role requirements. Candidates should confirm the exact package directly during the recruitment process.
π Preparation Tips
π§ Build a complete RAG project: Don’t stop at a simple PDF chatbot. Prepare an architecture involving ingestion, chunking, embeddings, vector search, retrieval, citations, evaluation, and monitoring.
π€ Understand agent orchestration: Build a small multi-step agent workflow using LangGraph or another framework and be able to explain state management, tool calling, retries, human intervention, and failure handling.
π Study MCP and A2A: Understand their purpose, how they help AI systems interact with tools or other agents, and where these approaches fit into an enterprise architecture.
π Strengthen Python: Revise asynchronous programming, APIs, classes, error handling, data processing, testing, and clean architecture.
βοΈ Prepare cloud deployment: Be ready to explain how you would deploy a GenAI service on AWS, Azure, or GCP while considering secrets, scaling, logging, latency, and cost.
π Learn AI evaluation: Prepare methods for measuring retrieval quality, groundedness, relevance, hallucination, latency, and response quality.
π Resume Tips
Your resume should immediately position you as a hands-on Generative AI Engineer, not simply someone who has completed an LLM course. Put Python, LLMs, RAG, Agentic AI, LangGraph/CrewAI/AutoGen, and cloud experience prominently in your skills section.
For each GenAI project, explain the actual architecture and your contribution. Mention the model used, RAG pipeline, vector database, agent workflow, tools, APIs, cloud platform, evaluation approach, and measurable outcomes where possible.
If you have worked on MCP or A2A, give these technologies dedicated visibility rather than hiding them in a large skills list. Also highlight production deployment, optimization, monitoring, security, and cost improvements.
π© How to Apply
π§ sujata.kadam@zensar.com
π Application Link: Click Here
Candidates with 2β4 years of relevant experience who can join immediately can share their updated CV at:
π§ sujata.kadam@zensar.com
In your application, highlight your GenAI experience, LLM/RAG projects, Agentic AI work, Python expertise, frameworks used, cloud platform exposure, MCP/A2A knowledge, current CTC, and immediate availability.
