Senior AI Engineer

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.

Requirements

Key Responsibilities

AI 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.