Senior AI Full Stack Engineer

Overview Senior AI Full Stack Engineer will design, build, and ship production-grade AI-powered applications that sit at the intersection of modern web engineering and the rapidly evolving world of generative AI and agentic systems.

We are looking for an engineer who is AI-native: someone who instinctively reaches for LLM APIs, RAG pipelines, multi-agent orchestration, and vector databases as core building blocks — while also owning the complete product surface from a performant React/Next.js front end through a scalable FastAPI or Node.js back end to cloud-deployed, observable production systems.

You will partner with AI Architects, data scientists, product managers, and UX designers to deliver AI-driven features across connected vehicle platforms, in-vehicle infotainment (IVI), manufacturing intelligence, and internal enterprise tools — serving customers and users at scale.

Responsibilities A DAY IN THE LIFE:

• Design and build end-to-end AI-powered product features — owning the full stack from React/Next.js UI through FastAPI/Node.js backend services to cloud infrastructure and LLM integrations
• Architect and implement LLM integration layers: connecting to OpenAI, Anthropic Claude, Google Gemini, Meta Llama, or other foundation models via APIs, fine-tuned endpoints, or on-device inference
• Build production-grade RAG (Retrieval-Augmented Generation) pipelines: document ingestion, chunking strategies, embedding generation, vector store management, and orchestrated retrieval for accurate, low-hallucination AI responses
• Develop multi-agent and agentic workflow systems using frameworks such as LangChain, LangGraph, CrewAI, or AutoGen — designing agent memory, tool use, planning loops, and goal decomposition
• Engineer prompt engineering strategies, guardrails, and context management systems that optimize LLM output for latency, cost, and quality at scale
• Build and maintain scalable microservices and event-driven backend architectures (Kafka, Redis, async queues) to handle high-throughput AI workloads and long-running agent tasks
• Design responsive, performant front-end experiences that elegantly surface AI capabilities — including real-time streaming responses (WebSocket/SSE), conversational UIs, AI-assisted dashboards, and multi-modal interfaces
• Establish observability and monitoring frameworks for AI production systems: model performance tracking, hallucination detection, token cost monitoring, latency profiling, and bias alerting
• Implement responsible AI controls at the application layer: input/output guardrails, content filtering, PII redaction, rate limiting, and audit logging for regulatory compliance.
• Integrate AI features into automotive-domain applications including connected vehicle dashboards, IVI systems, manufacturing quality intelligence platforms, and supply chain optimization tools
• Collaborate with AI Architects to translate architecture blueprints into production code; provide engineering feedback that improves architectural decisions
• Champion engineering excellence: code reviews, automated testing (unit, integration, AI evaluation), CI/CD pipelines, and documentation for AI-enabled features.

Must-haves

• Bachelor’s degree in Computer Science, Software Engineering, or related technical field; Master’s degree a plus.
• 7+ years of professional full stack engineering experience with at least 2+ years building and shipping production AI/LLM-integrated features.
• Proven track record delivering AI-powered products to real users at scale — prototypes do not count
• Expert-level proficiency in React and Next.js (App Router, SSR, SSG, streaming); TypeScript required.
• Experience building real-time AI interfaces: streaming LLM responses via WebSocket or
• Server-Sent Events (SSE), conversational chat UIs, and multi-modal content displays.
• trong command of modern CSS, state management (Zustand, Redux Toolkit, or Jotai), and UI component libraries. Strong Python backend development using FastAPI (preferred) or equivalent; experience building async, high-throughput REST and streaming APIs. Solid understanding of microservices design patterns: event-driven architecture, message queues (Kafka, Redis Pub/Sub, Celery/Taskiq), and fault-tolerant distributed systems. Database proficiency: PostgreSQL, MongoDB, and Redis for caching and session management.
• Hands-on production experience integrating LLM APIs: OpenAI GPT-4o, Anthropic Claude, Google Gemini, Meta…