Senior Software Engineer (Backend) - Gen AI

Key Skills: Java, Spring Boot, RESTful APIs, Microservices, LLM Integration, LangChain, RAG, AI Workflows, MySQL, NoSQL, Distributed Systems Roles and Responsibilities: Architect and Build: Design, develop, and maintain robust, scalable backend services for Gen AI platforms, integrating state-of-the-art models and techniques. LLM Integration: Implement and optimize backend APIs for interacting with leading Large Language Models (LLMs) such as Gemini, Claude, GPT series, Grok, and others. Agentic System Design: Architect and implement common agentic patterns (e.g., orchestrators, generator-evaluator) using frameworks like LangChain, LangGraph, or n8n to build sophisticated AI workflows. Advanced AI Techniques: Utilize Retrieval-Augmented Generation (RAG), advanced context engineering, and efficient caching strategies (both implicit and explicit) to enhance model accuracy and performance. Cost & Performance Optimization: Conduct GenAI cost-benefit analysis to select the right models and architectures, ensuring solutions are powerful and economically efficient at scale. Full Lifecycle Ownership: Own features from conception to deployment, including unit testing, code reviews, and ensuring smooth release cycles in a CI/CD environment. Mentorship: Guide and mentor junior engineers, fostering a culture of technical excellence and innovation in the AI space. Skills Required: Experience: 2.5 to 5 years of hands-on experience in backend development, with a focus on large-scale, consumer-facing applications. Backend Proficiency: Strong coding skills and deep expertise in Java, Spring, and Spring Boot, with experience in building RESTful APIs and microservices architecture. Database Skills: Proficient with relational databases like MySQL and one or more NoSQL databases (e.g., Redis, MongoDB, Cassandra). CS Fundamentals: Excellent grasp of Data Structures, Algorithms, and problem-solving skills. Distributed Systems: Proven experience with distributed systems, caching, and handling large volumes of data. Preferred Skills (Gen AI & ML): Prompt Engineering: Expertise in advanced prompt engineering techniques such as Chain-of-Thought (CoT), Self-Correction, and Few-shot prompting. Gen AI Frameworks: Hands-on experience with AI development frameworks like LangChain, LangGraph, or LlamaIndex. Vector Databases: Familiarity with vector databases such as Qdrant, Pinecone, etc., for efficient similarity search. Multi-modality: Experience integrating multi-modal models for image or video generation (e.g., Veo, Kling) is a strong plus. Recommender Systems: Understanding of recommender systems and personalization algorithms. Python Knowledge: Working knowledge of Python is highly desirable. Education: Bachelor's or Master's in Computer Science, Engineering, AI, ML, or related fields