Lead Fullstack AI Development Engineer

• We are seeking a highly talented and seasoned Lead Full-Stack Development Engineer with 8–12 years of
• deep software engineering and machine learning production experience to drive the technical execution of our
• AI product suites. Operating embedded directly within the AI Product & Engineering team, this pivotal role
• serves as the primary engineering anchor responsible for designing, building, and deploying scalable,
• high-performance AI applications, interactive user interfaces, and robust production systems.
• You will lead the end-to-end development lifecycle, bridging the gap between foundational AI research models,
• cloud data infrastructure, and user-facing software applications. Your core mission is to architect resilient
• codebases, optimize inference pipelines, implement modern user interfaces, and establish automated DevOps
• guardrails that seamlessly transform cognitive AI capabilities into enterprise-grade, scalable business solutions.

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.

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).