Senior AI Engineer

Weekday AI; Bengaluru; On-site; INR 1,200,000 - 2,500,000; Full time

Job summary

Weekday AI is seeking a senior AI engineer/architect to design, develop, and deploy enterprise-grade AI and Generative AI applications. You will build intelligent solutions powered by LLMs and advanced AI architectures, and develop scalable AI workflows using orchestration frameworks and agent-based systems.

The role requires translating business requirements into practical AI solutions, establishing best practices for AI architecture, development, testing, and deployment, and implementing

Qualifications

• 5+ years of experience in software engineering or AI engineering.

Responsibilities

• Design, develop, and deploy enterprise-grade AI and Generative AI applications.
• Build intelligent solutions powered by LLMs and advanced AI architectures.
• Develop scalable AI workflows using orchestration frameworks and agent-based systems.
• Translate business requirements into practical, high-impact AI solutions.
• Establish best practices for AI architecture, development, testing, and deployment.
• 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.
• Architect and deploy AI solutions on Microsoft Azure Cloud environments.
• Develop cloud-native services, APIs, and microservices supporting AI workloads.

Skills

Python

Java

AI frameworks

LLMs

Prompt engineering

RAG

Agentic AI

A2A architectures

Vector embeddings

Knowledge retrieval

Tools

Azure Cloud

Cosmos DB

Redis

Kubernetes

CI/CD

MLOps

Vector DB

Job 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

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.