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Lead Fullstack AI Development Engineer

Diebold Nixdorf
Hyderabad, IND
Full-time
Lead · 8–12 years experience
Onsite
Discovered 2 weeks ago
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Full Job Posting

Required Qualifications

  • Professional Experience: 8–12 years of proven success in full-stack software engineering roles, with at least 3+ years actively leading technical teams and deploying commercial AI/ML-powered web products into production environments. • Front-End Engineering: Expert proficiency in React.js, Next.js, TypeScript, and modern state management libraries (e.g., Redux, Zustand) alongside HTML5, CSS3, and Tailwind CSS. • Core Backend Languages: Expert-level software development proficiency in Python, Java, or Go, paired with deep experience in enterprise backend development environments and API design (RESTful, GraphQL, WebSockets). • AI & Machine Learning Frameworks: Advanced proficiency building with orchestration and deep learning frameworks, specifically LangChain, LlamaIndex, PyTorch, TensorFlow, and agentic platforms (e.g., AutoGen, CrewAI).
  • CI/CD & MLOps Infrastructure: Robust experience managing application containerization, cluster orchestration, and automated pipelines utilizing Docker, Kubernetes, GitHub Actions, GitLab CI, or Jenkins. • AI & Database Ecosystems: Hands-on expertise with vector databases modern database stacks, and large-scale semantic search infrastructures. • Software Design Patterns: Superior understanding of distributed systems architecture, microservices, asynchronous event-driven programming, and caching strategies. • Agile Engineering Leadership: Documented success leading engineering sprints, mentoring mid-to-senior frontend and backend developers, and serving as the ultimate system-level Subject Matter Expert (SME) to achieve delivery goals
  • Good business English skills (Written and spoken).

Responsibilities

  • Full-Stack AI Product Architecture & Development • Lead the system design and full-stack development of production-grade AI applications, responsive front-end interfaces, microservices, and user-centric features. • Architect scalable APIs and orchestration layers that connect core business logic with upstream Large Language Models (LLMs), agentic workflows, and predictive systems. • Establish rigorous engineering standards for code quality, comprehensive code reviews, automated testing, and version control across the full development stack. Front-End Interface & User Experience Engineering • Architect high-performance web applications using modern front-end frameworks, ensuring exceptional responsiveness, state management, and real-time data streaming (e.g., streaming LLM token responses). • Collaborate closely with UI/UX engineers and Product Managers to convert design systems and visual wireframes into reusable, accessible component libraries. • Optimize front-end application layers for speed, cross-browser compatibility, and seamless integration with complex backend application logic.
  • Model Deployment & RAG/LLM Optimization • Implement and optimize enterprise-scale frameworks for Retrieval-Augmented Generation (RAG), multi-agent orchestration, and prompt engineering pipelines. • Integrate and manage custom vector databases ensuring sub-second API responses through efficient indexing, caching, and database query optimization
  • Ensures team-wide application of standard operating procedures for development, deployment, support, break-fix and systems management.
  • Assesses usage and utilization trends to make design and implementation recommendations toward scaling the company's architecture.
  • Writes and maintains software build systems and supporting tools including continuous integration, packaging and deployment.
  • collaborates with others to troubleshoot and resolve escalated production, integration or application issues.
  • Full-Stack AI Product Architecture & Development • Lead the system design and full-stack development of production-grade AI applications, responsive front-end interfaces, microservices, and user-centric features. • Architect scalable APIs and orchestration layers that connect core business logic with upstream Large Language Models (LLMs), agentic workflows, and predictive systems. • Establish rigorous engineering standards for code quality, comprehensive code reviews, automated testing, and vers

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