Senior AI Engineer at Weekday AI

Delhi, delhi, India -

Full Time

Start Date

Immediate

Expiry Date

11 Sep, 26

Salary

2500000.0

Posted On

13 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Java, Large Language Models, Agentic AI, RAG, Microsoft Azure, Vector Databases, Prompt Engineering, Microservices, Kubernetes, CI/CD, MLOps, Semantic Search, MCP Protocol, Distributed Systems, Cloud Architecture

Industry

technology;Information and Internet

Description

This role is for one of the Weekday's clients Salary range: Rs 1200000 - Rs 2500000 (ie INR 12-25 LPA) Experience: 5+ yrs Location: Gurgoan, delhi, bangalore Job Type: full-time We are looking for an experienced Senior AI Engineer to lead the design, development, and deployment of advanced AI-powered applications leveraging Large Language Models (LLMs), Agentic AI frameworks, and cloud-native technologies. This role is ideal for professionals who are passionate about building intelligent, scalable, and production-ready AI systems that solve complex business challenges. As a Senior AI Engineer, you will play a key role in architecting and delivering next-generation AI solutions, including multi-agent systems, Retrieval-Augmented Generation (RAG) platforms, intelligent automation workflows, and enterprise-grade GenAI applications. You will work closely with product managers, data engineers, architects, and business stakeholders to transform business requirements into innovative AI-driven solutions. The ideal candidate combines strong AI engineering expertise with deep software development skills, cloud architecture knowledge, and hands-on experience building scalable AI applications using modern frameworks and technologies. You should be comfortable working across the entire AI lifecycle—from model integration and orchestration to deployment, monitoring, optimization, and governance. This role offers the opportunity to work on cutting-edge AI technologies, build impactful solutions at scale, and contribute to the evolution of enterprise AI platforms and intelligent systems. Key ResponsibilitiesAI Solution Design & Development Design, develop, and deploy enterprise-grade AI and Generative AI applications. Build intelligent solutions powered by Large Language Models (LLMs) and advanced AI architectures. Develop scalable AI workflows using modern orchestration frameworks and agent-based systems. Translate business requirements into practical, high-impact AI solutions. Establish best practices for AI application architecture, development, testing, and deployment. Agentic AI & Multi-Agent Systems Design and implement sophisticated Agentic AI solutions capable of autonomous task execution. Build and orchestrate multi-agent workflows using Agent-to-Agent (A2A) communication frameworks. Develop intelligent agents that collaborate, reason, and execute complex business processes. Integrate MCP protocols and advanced orchestration mechanisms for seamless agent interactions. Optimize agent performance, scalability, and reliability across enterprise environments. LLM Engineering & RAG Architecture Build Retrieval-Augmented Generation (RAG) systems to enhance AI accuracy and contextual understanding. Develop prompt engineering and context engineering strategies to maximize model effectiveness. Implement vector embedding pipelines and semantic search capabilities. Integrate and optimize LLMs for various enterprise use cases. Design scalable knowledge retrieval frameworks utilizing vector databases and search technologies. Cloud-Native AI Platforms Architect and deploy AI solutions on Microsoft Azure Cloud environments. Develop cloud-native services, APIs, and microservices supporting AI workloads. Build and manage serverless applications and containerized AI services. Ensure high availability, security, scalability, and performance of deployed AI systems. Implement cloud best practices for monitoring, governance, and operational excellence. Data & Platform Engineering Integrate AI solutions with enterprise data platforms and storage systems. Work with vector databases, search services, caching platforms, and distributed data stores. Design efficient data pipelines supporting AI model inference and retrieval workloads. Optimize data access, storage strategies, and performance for large-scale AI applications. Ensure data quality, consistency, and reliability across AI ecosystems. Performance Optimization & Collaboration Monitor AI applications for latency, accuracy, scalability, and cost efficiency. Troubleshoot complex technical issues and implement performance improvements. Collaborate closely with engineering, product, and business teams throughout project lifecycles. Drive technical innovation and contribute to AI architecture standards and governance frameworks. Mentor team members and share best practices across AI engineering initiatives. What Makes You a Great Fit 6–9 years of experience in Software Engineering, AI Engineering, Machine Learning, or related technical domains. Strong proficiency in Python and working knowledge of Java. Hands-on experience building AI and Generative AI applications using modern AI frameworks. Strong expertise in Agentic AI frameworks and Agent-to-Agent (A2A) architectures. Experience implementing MCP protocol integrations and multi-agent communication systems. Deep understanding of Large Language Models (LLMs), prompt engineering, and context engineering. Proven experience designing and implementing Retrieval-Augmented Generation (RAG) solutions. Expertise in vector embeddings, semantic search, and knowledge retrieval systems. Strong experience with Microsoft Azure Cloud and cloud-native application development. Familiarity with Azure AI services, vector databases, Redis, Cosmos DB, and related technologies. Experience building scalable distributed systems and microservices architectures. Understanding of cloud-native design principles, scalability, and performance optimization. Knowledge of containerization, Kubernetes, CI/CD, and MLOps practices is advantageous. Familiarity with AI governance, security, observability, and responsible AI principles. Strong analytical, problem-solving, and architectural thinking abilities. Excellent communication and stakeholder management skills. Ability to work independently while driving innovation in a fast-paced, technology-driven environment.

Responsibilities

Lead the design, development, and deployment of enterprise-grade AI applications using LLMs and Agentic AI frameworks. Architect scalable RAG platforms and multi-agent systems while collaborating with stakeholders to solve complex business challenges.