Lead AI Fullstack Development Engineer

Job Description

We are seeking a highly talented and seasoned Lead Full-Stack Development Engineer with 812 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.

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.

Qualifications

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.
  …