DEandA - AIML - Data Science - Artificial Intelligence Of Things AIOT

Zensar Technologies; Pune District; On-site; INR 2,000,000 - 4,000,000; Full time

Job summary

Zensar Technologies in Pune is seeking a senior AI/ML engineer to design and implement enterprise RAG applications using LLMs, embeddings, and hybrid search. You will build end-to-end document and knowledge ingestion pipelines, create robust retrieval and context management strategies, and optimize for accuracy and cost.

Collaboration with architects, analysts, and domain experts will drive scalable AI solutions.

Qualifications

• Strong understanding of LLMs and Generative AI concepts and practices.
• Experience with prompt engineering, structured prompting, and tool calling.
• Hands-on work building RAG applications, document ingestion, and embeddings.
• Knowledge of vector/search platforms and retrieval optimization.
• Familiarity with Azure OpenAI, Azure AI Search, OpenSearch, or similar.

Responsibilities

• Design and develop enterprise RAG applications using LLMs, embeddings, and hybrid search.
• Build end-to-end document ingestion and knowledge pipelines for structured data.
• Implement parsing, chunking, metadata enrichment, indexing, and retrieval strategies.
• Design and optimize semantic, vector, and hybrid search solutions.
• Develop RAG workflows including query understanding, rewriting, retrieval, and grounding.
• Work with LLMs such as Azure OpenAI and Anthropic Claude or equivalents.
• Develop agentic AI solutions using tools, MCP, and multi-agent architectures.
• Establish RAG evaluation, observability, and monitoring for latency, quality, and cost.
• Optimize RAG apps for accuracy, latency, scalability, and cost efficiency.
• Integrate RAG apps with enterprise systems, APIs, databases, and knowledge sources.
• Develop secure APIs and backend services for AI applications.
• Collaborate with architects, analysts, and domain experts to translate requirements.
• Establish best practices around prompt engineering, context management, and guardrails.
• Troubleshoot production issues and iterate based on feedback and metrics.

Skills

LLMs & Gen AI

Prompt engineering

Function calling

Context management

Hallucination grounding

RAG applications

Embeddings generation

Vector search

Retrieval optimization

Azure OpenAI

Python development

REST APIs

FastAPI / Flask

API integrations

Tools

LangGraph

Google ADK

MCP (Model Context Protocol)

Job description

Key Responsibilities

• Design and develop enterprise RAG applications using LLMs, embeddings, vector databases, and hybrid search.
• Build end-to-end document ingestion and knowledge ingestion pipelines for structured and unstructured data.
• Implement document parsing, chunking, metadata enrichment, embeddings, indexing, and retrieval strategies.
• Design and optimize semantic, vector, keyword, and hybrid search solutions.
• Develop RAG workflows incorporating query understanding, query rewriting, retrieval, reranking, context generation, and response generation.
• Work with LLMs such as Azure OpenAI, Anthropic Claude, or equivalent models.
• Develop agentic AI solutions using tools, function calling, MCP, and multi-agent/single-agent architectures where appropriate.
• Implement RAG evaluation and observability including retrieval quality, answer relevance, groundedness, hallucination detection, latency, and token/cost monitoring.
• Optimize RAG applications for accuracy, latency, scalability, and cost.
• Integrate RAG applications with enterprise systems, APIs, databases, repositories, and knowledge sources.
• Develop secure APIs and backend services for AI applications.
• Collaborate with architects, developers, business analysts, and domain experts to translate business requirements into AI solutions.
• Establish best practices around prompt engineering, context management, guardrails, security, and responsible AI.
• Troubleshoot production issues and continuously improve the AI application based on user feedback and evaluation metrics.

Required Technical Skills

Generative AI / LLM

• Strong understanding of LLMs and Generative AI
• Prompt engineering and structured prompting
• LLM inference and model selection
• Function calling / tool calling
• Context-window management
• Understanding of hallucination and grounding challenges

RAG

• Strong hands-on experience building RAG applications
• Document ingestion and preprocessing
• Chunking strategies
• Metadata design and filtering
• Embedding generation
• Vector search
• Hybrid search
• Reranking
• Query expansion / rewriting
• Retrieval optimization
• RAG evaluation

AI / Agentic Frameworks

• Experience with one or more frameworks such as:
  • LangGraph
  • Google ADK
• Experience with MCP (Model Context Protocol) is a plus.
• Understanding of agent orchestration and tool-based workflows.

Cloud & Search

• Strong experience with Microsoft Azure
• Azure OpenAI / Azure AI Foundry
• Azure AI Search or equivalent vector search platform
• Azure Blob Storage
• Azure App Service / Functions
• API Management
• Experience with AWS AI services or Amazon OpenSearch is a plus.

Programming

• Strong Python development skills
• REST API development
• Flask / FastAPI
• JSON and API integrations
• Experience with SQL and relational databases

Databases / Search

• Vector databases/search engines such as:
  • Azure AI Search
  • OpenSearch
  • PostgreSQL/pgvector
  • Pinecone
  • Elasticsearch
  • Weaviate
• Understanding of indexing and search optimization.

RAG Evaluation & Observability

Experience with AI observability and evaluation tools such as:

• Arize Phoenix
• LangSmith
• Azure AI evaluation capabilities
• RAGAS
• Custom evaluation frameworks

Knowledge of metrics such as:

• Context relevance
• Context precision/recall
• Answer relevance
• Faithfulness / groundedness
• Retrieval accuracy
• Hallucination rate
• Latency
• Token consumption
• Cost per request

Preferred / Good-to-Have Skills

• Experience with Guidewire PolicyCenter, ClaimCenter, BillingCenter, or other enterprise insurance platforms.
• Experience working with large technical documentation repositories.
• Understanding of Guidewire data models, APIs, configuration, and data dictionaries.
• Experience building AI assistants for enterprise developers.
• Experience with structured knowledge extraction from HTML, XML, JSON, PDFs, source code, database schemas, and technical documentation.
• Knowledge of enterprise security, RBAC, PII protection, and data governance.

Experience with semantic caching and

Qualifications

At Zensar, we're "experience-led everything". We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: "Together, we shape experiences for better futures". Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus.

Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensarand join us to Grow. Own. Achieve. Learn.to be the best version of yourself.

We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.