Senior Software Engineer at Weekday AI

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

16 Oct, 26

Salary

5000000.0

Posted On

19 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Java, Spring Boot, Kafka, Microservices, Distributed Systems, LLM Integration, AWS, MySQL, MongoDB, LangChain, LangGraph, LlamaIndex, CrewAI, AI Gateway Architecture, Model Context Protocol, Agent Orchestration

Industry

technology;Information and Internet

Description

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟯𝟱𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟱𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟯𝟱-𝟱𝟬 𝗟𝗣𝗔) Experience: 5+ yrs Location: Bengaluru, Karnataka, India Job Type: Full-time We are seeking a highly skilled Senior Software Engineer – Backend to design, build, and scale next-generation AI platform services that power enterprise-grade intelligent applications. This role is ideal for engineers with strong expertise in Java, cloud-native microservices, distributed systems, and AI platform architecture, along with hands-on experience integrating Large Language Models (LLMs), agent orchestration frameworks, and modern backend technologies. As a senior member of the engineering team, you will own the development of scalable backend services, AI gateways, and orchestration frameworks while collaborating with cross-functional teams to deliver secure, reliable, and high-performance solutions. The role demands strong technical leadership, excellent problem-solving skills, and a passion for building production-ready AI-powered platforms. Key Responsibilities Design, develop, and maintain production-grade backend services for AI platforms, including LLM gateways, model integration services, and agent orchestration frameworks. Build scalable cloud-native microservices using Java, Spring Boot, Kafka, and modern distributed system design principles. Architect and implement AI gateway capabilities such as multi-provider model routing, intelligent failover, token management, cost optimization, request logging, and observability. Develop infrastructure supporting Model Context Protocol (MCP), tool integration, and intelligent agent workflows. Design and implement multi-step AI reasoning workflows, tool orchestration, guardrails, and human-in-the-loop processes for enterprise AI applications. Take end-to-end ownership of critical backend components, ensuring scalability, security, reliability, and maintainability. Diagnose and resolve complex performance bottlenecks across backend services, AI inference layers, and distributed architectures. Collaborate with Product, AI/ML, QA, UX, and Engineering teams to design and deliver high-quality software solutions. Mentor junior engineers through technical guidance, code reviews, architecture discussions, and engineering best practices. Promote software quality through clean code, automated testing, performance optimization, and continuous improvement initiatives. What Makes You a Great Fit 5+ years of professional experience in backend software engineering with strong expertise in Java and object-oriented programming. Proven experience building and scaling cloud-native microservices and enterprise backend applications using Spring Boot, Kafka, and distributed architectures. Strong understanding of relational and NoSQL databases, including MySQL, MongoDB, and modern data management practices. Experience developing enterprise-scale applications with strong knowledge of performance tuning, debugging, monitoring, and backend optimization. Hands-on experience with cloud platforms such as AWS and familiarity with observability tools, logging frameworks, and monitoring solutions. Solid understanding of Large Language Models (LLMs), AI platform architecture, and intelligent application development. Experience or strong knowledge of Model Context Protocol (MCP), AI gateways, multi-model routing, semantic caching, and AI service orchestration. Familiarity with agentic AI frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, OpenAI Agents SDK, or similar orchestration technologies. Understanding of AI observability, evaluation techniques, prompt execution monitoring, AI safety practices, content filtering, and hallucination mitigation. Excellent analytical thinking, communication, mentoring, and collaboration skills with the ability to thrive in a fast-paced engineering environment while delivering high-quality, production-ready solutions.

Responsibilities

Design and develop production-grade backend services and AI gateways to power enterprise-grade intelligent applications. Lead the implementation of scalable microservices, AI reasoning workflows, and agent orchestration frameworks while mentoring junior engineers.