Full Stack Developer – AI

Accountabilities: Full Stack Development: Develop, maintain, and enhance frontend applications, backend services, APIs, and microservices to support production GenAI solutions. Generative AI Integration: Implement and support AI-powered features using Large Language Models (LLMs), RAG pipelines, prompt engineering, and related technologies. Production Support and Troubleshooting: Diagnose and resolve complex application and platform issues, perform root cause analysis (RCA), and implement corrective actions to minimize recurring incidents. Performance Optimization: Improve application responsiveness, scalability, reliability, and overall performance across production environments. Cloud and Infrastructure: Support application deployment and operations on Azure and/or AWS, applying best practices for cloud environments, containerization, and infrastructure management. CI/CD and DevOps: Implement and maintain continuous integration and continuous delivery pipelines using tools and practices involving Docker, Kubernetes, and automated deployment workflows. Data Management: Develop and maintain database integrations, data ingestion processes, and supporting infrastructure for SQL, NoSQL, and vector database technologies. AI Reliability and Quality: Monitor and address GenAI reliability challenges, including hallucinations, model drift, bias, and other factors affecting application quality and user trust. Cross-Functional Collaboration: Partner with engineering, support, and platform teams to resolve incidents, deliver application enhancements, and improve operational efficiency. Continuous Improvement: Identify opportunities to strengthen application availability, reduce incident recurrence, and improve engineering processes and production support practices. Requirements Full Stack Development: Strong experience in full stack software development, including frontend and backend application engineering. Frontend Technologies: Practical experience with React, Angular, and Node.js for building and maintaining modern applications and services. Generative AI Expertise: Hands-on experience with LLMs, RAG pipelines, and prompt engineering, along with an understanding of GenAI application architecture. AI Lifecycle Knowledge: Familiarity with AI model lifecycle management, integration patterns, and the challenges associated with deploying AI-powered applications in production. Cloud Platforms: Experience working with Microsoft Azure and/or AWS cloud environments. DevOps and Containerization: Knowledge of CI/CD pipelines, Docker, Kubernetes, and modern software deployment practices. Database Technologies: Strong understanding of SQL and NoSQL databases, with familiarity with vector databases and data ingestion frameworks. Production Operations: Experience supporting production applications, troubleshooting complex technical issues, conducting root cause analysis, and implementing sustainable fixes. AI Quality and Reliability: Awareness of AI-specific risks, including hallucinations, model drift, bias, and reliability challenges. Analytical Skills: Strong problem-solving, debugging, and performance optimization capabilities, with a structured approach to technical challenges. Communication and Collaboration: Excellent communication skills and the ability to work effectively with cross-functional teams in a fast-paced technical environment. Work Approach: A proactive, quality-focused mindset, with the ability to balance application development, operational support, and continuous improvement. Benefits Contract Duration: 12-month contract, with a stated start date of October 26, 2026, and end date of October 26, 2027. Compensation: Advertised hourly pay range of $15–$20 per hour . Work Location: Hyderabad, Telangana, India, with the opportunity to work remotely across India; the original listing also mentions onsite work as a possible arrangement. Technical Exposure: Work with modern Generative AI technologies, including LLMs, RAG pipelines, and prompt engineering. Cloud and Engineering Experience: Build practical expertise in Azure and/or AWS, microservices, CI/CD, containerization, and scalable application architecture. Production Engineering: Gain experience improving the reliability, performance, and operational stability of real-world AI applications. Cross-Functional Collaboration: Work alongside engineering, support, and platform teams to solve complex technical challenges. Professional Development: Strengthen your skills in full stack development, AI integration, cloud infrastructure, and production support.