Job description

Job Description 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. • Strong 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 Llama, or Mistral. • Deep expertise in RAG architecture: document processing, embedding models, chunking strategies, semantic search, vector databases (Pinecone, Weaviate, Chroma, pgvector, Qdrant). • Experience with agentic AI frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or OpenAI Agents SDK. • Strong prompt engineering and context engineering skills; experience designing multi-turn conversations, tool-calling workflows, and structured LLM output parsing. • Experience implementing LLM guardrails, hallucination mitigation, and output validation for production systems. • Strong experience with at least one major cloud platform: AWS, Azure, or GCP; familiarity with managed AI/ML services (AWS Bedrock, Azure OpenAI Service, Vertex AI). • Containerization and orchestration: Docker and Kubernetes; experience with Helm charts and cloud-native deployments. • CI/CD pipelines for AI-enabled products: automated testing, model evaluation gates, and zero-downtime deployments. • AI observability tooling: LangSmith, Weights & Biases, Helicone, or Arize for LLM tracing, cost tracking, and quality monitoring. • General observability: OpenTelemetry, Prometheus, Grafana, or Datadog for distributed tracing, metrics, and alerting.

Requirements

Must-haves

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
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.
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.
Strong command of modern CSS, state management (Zustand, Redux Toolkit, or Jotai), and UI component libraries.
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 Llama, or Mistral.
Deep expertise in RAG architecture: document processing, embedding models, chunking strategies, semantic search, vector databases (Pinecone, Weaviate, Chroma, pgvector, Qdrant).
Experience with agentic AI frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or OpenAI Agents SDK.

Nice-to-haves

Bachelor’s degree in Computer Science, Software Engineering, or related technical field; Master’s degree a plus.
Strong Python backend development using FastAPI (preferred) or equivalent; experience building async, high-throughput REST and streaming APIs.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmreactnext.jsnode.jsci/cdtypescriptpythonrestawsazuregcpmldockerkubernetes

Platform: careers-browser
Effort: medium