Senior AI Engineer at Weekday

Gurugram, haryana, India -

Full Time

Start Date

Immediate

Expiry Date

12 Sep, 26

Salary

2500000.0

Posted On

14 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Agentic AI Frameworks, A2A Frameworks, Azure Cloud, MCP Protocol, Python, Java, LangChain, LangGraph, Prompt Engineering, RAG Architectures, Vector Embeddings, Azure AI Search, Azure Functions, Azure Container Apps, Cloud Native Architecture, LLM Integration

Industry

Retail Apparel and Fashion

Description

This role is for one of our clients Company Name: Whilter.AI Industry: Technology, Information and Media Seniority level: Mid-Senior level Experience: 5+ yrs Location: Gurgoan, delhi, bangalore Job Type: full-time \n \n ₹12,00,000 - ₹25,00,000 a year Position: Senior AI Engineer Experience: 6–9 Years Location: Gurgaon & Bangalore (Hybrid) Employment Type: Full-Time About the Role We are seeking a highly skilled Senior AI Engineer to design, develop, and deploy next-generation AI applications powered by Large Language Models (LLMs), Agentic AI frameworks, and cloud-native architectures. The ideal candidate will have deep expertise in AI/ML engineering, Agent-to-Agent (A2A) systems, MCP protocol integration, and scalable Azure-based deployments. This role requires hands-on experience building production-grade AI solutions using modern frameworks such as LangChain and LangGraph, along with strong software engineering and cloud architecture skills. Key Responsibilities Design, develop, and deploy enterprise-grade AI/GenAI solutions leveraging LLMs and Agentic AI architectures. Build and orchestrate multi-agent workflows using Agentic Layer A2A frameworks and MCP Protocol. Develop intelligent applications utilizing vector embeddings, prompt engineering, context engineering, and retrieval strategies. Create scalable AI pipelines using LangChain, LangGraph, and related AI orchestration frameworks. Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases and search platforms. Deploy and manage AI services on Azure Cloud, ensuring high availability, security, and performance. Develop and maintain Azure Functions, Azure Container Apps, and cloud-native microservices. Integrate and optimize data storage solutions including Azure AI Search, VectorDBs, Redis, Cosmos DB, Blob Storage, and Iceberg. Collaborate with product, engineering, and data teams to translate business requirements into AI-driven solutions. Monitor, troubleshoot, and optimize AI systems for scalability, latency, accuracy, and cost efficiency. Establish best practices for AI application architecture, testing, deployment, and governance. Required Skills & Qualifications Must Have 6–9 years of experience in Software Engineering, AI/ML Engineering, or related domains. Strong hands-on experience with Python and proficiency in Java. Experience building AI/GenAI applications using LangChain and LangGraph. Expertise in: Prompt Engineering Context Engineering Vector Embeddings RAG Architectures LLM Integration Hands-on experience with Agentic AI frameworks, Agent-to-Agent (A2A) communication, and MCP Protocol. Strong experience deploying solutions on Microsoft Azure Cloud. Experience with: Azure AI Search Vector Databases Redis Cosmos DB Experience building and managing: Azure Functions Azure Container Apps Strong understanding of cloud-native architectures, distributed systems, scalability, and performance optimization. Good to Have Experience with Azure Blob Storage and Apache Iceberg. Exposure to MLOps and AI observability tools. Experience with Kubernetes, Docker, and CI/CD pipelines. Knowledge of multi-agent orchestration and autonomous AI systems. Familiarity with AI security, governance, and responsible AI practices. Must-have skills Agentic AI Frameworks, A2A framwords, Azure Cloud Good-to-have skills MCP protocall, Cloud native architecture, PYthon java \n

Responsibilities

Design and deploy enterprise-grade AI solutions using LLMs, Agentic AI architectures, and multi-agent workflows. Manage scalable AI pipelines and cloud-native microservices on the Microsoft Azure platform.