Sr. AI Engineer

Sr. AI Engineer

Location: Hyderabad, INDIA

Work Mode: Hybrid

Skills: Python;Azure;LLMs;Gen AI;Agentic AI;LangChain;LangGraph;Semantic Kernel;MS Auto Gen

Experience in Years: 10

Type: Full Time

Sr. AI Engineer - Generative AI Agentic AI

Location: Hyderabad (Hybrid)

1. JOB PURPOSE:

• We are looking for an experienced AI Engineer responsible for designing, developing, and deploying Generative
• AI and Agentic AI solutions that support enterprise-scale business use cases. The role will build intelligent
• systems that integrate complex backend services and client-facing applications across web, mobile and
• enterprise platforms.
• The primary responsibility is to design and develop AI-powered applications, autonomous agents and multi-agent
• workflows while coordinating with cross-functional teams across architecture, engineering, product and business
• functions. A commitment to collaborative problem solving, sophisticated design and product quality is essential.
• This role requires strong hands-on engineering experience, practical working knowledge of modern agent
• frameworks, and the ability to deliver secure, scalable, observable and governed AI solutions in cloud-native
• environments.

2. KEY ACCOUNTABILITIES:

Generic Accountability

• Design and build enterprise-grade Generative AI applications using Large Language Models (LLMs) across business and technology use cases.

• Develop Agentic AI solutions including autonomous agents, multi-agent orchestration, tool-driven workflows and decision support systems.

• Implement robust Retrieval-Augmented Generation (RAG) architectures using embeddings, vector

databases, semantic search and optimized retrieval strategies.

• Integrate AI services with enterprise APIs, middleware, backend systems and client channels such as web and mobile applications.

• Apply prompt engineering, tool/function calling, memory management, context engineering and agent orchestration patterns to improve quality, latency and cost efficiency.

• Deploy scalable AI workloads on Azure and AWS cloud platforms using modern DevOps, CI/CD, container and Kubernetes practices.

• Establish model evaluation, guardrails, observability, security controls and AI governance practices aligned to enterprise standards.

• Collaborate with product owners, enterprise architects, engineering teams and business stakeholders to convert business needs into secure, reusable and sustainable AI capabilities.

• Discover technical debt and continuous improvement opportunities in existing systems and influence the technical backlog with architecture and product stakeholders.

• Mentor engineers, promote engineering craftsmanship, and support communities of practice for AI engineering, cloud-native development and automation.

3. JOB CONTEXT

Specific Accountability

• Build production-grade AI agents using LangChain, LangGraph, Microsoft Semantic Kernel and Microsoft

AutoGen; working experience with Semantic Kernel and AutoGen is mandatory.

• Design and implement multi-agent workflows covering planning, tool use, state management, memory, feedback loops, human-in-the-loop controls and workflow-driven decisions.

• Architect end-to-end data indexing pipelines optimized for semantic search, RAG quality, retrieval latency and answer relevancy.

• Design resilient data ingestion frameworks that ensure high availability and low latency for vectorized datasets.

• Implement context engineering strategies to reduce token utilization, manage prompt/context windows and improve end-to-end inference latency. - Evaluate and improve AI agent performance using relevant metrics including response quality, grounding, retrieval precision/recall, faithfulness, latency, cost, safety and task completion.

• Deploy and manage models using Azure OpenAI Service, Amazon Bedrock and Google Vertex AI where required by solution constraints.

• Integrate pre-built AI APIs for speech-to-text, computer vision and natural language processing where they add business value.

• Leverage serverless architectures such as AWS Lambda and Azure Functions to trigger AI inference, data pre-processing and asynchronous workflow steps.

• Implement CI/CD pipelines with code quality checks, static code analysis, security scanning, requirement traceability and Jira integration.

• Operate monitoring, logging and alerting for AI, application and cloud environments with measurable reliability and supportability. …