Senior AI Full Stack Engineer

About the role

The Senior AI Full Stack Engineer will design, build, and ship production-grade AI-powered applications that integrate with modern web engineering and the latest advancements in generative AI and agentic systems. The ideal candidate will bring a strong background in full-stack engineering, particularly in building and deploying AI/LLM-integrated features to real users at scale.

Responsibilities

• 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 various foundation models via APIs, fine-tuned endpoints, or on-device inference.

• Build production-grade RAG (Retrieval-Augmented Generation) pipelines, including document ingestion, chunking strategies, embedding generation, vector store management, and orchestrated retrieval.

• Develop multi-agent and agentic workflow systems using frameworks like LangChain, LangGraph, CrewAI, or AutoGen, focusing on agent memory, tool use, planning loops, and goal decomposition.

• Engineer prompt engineering strategies, guardrails, and context management systems to optimize LLM output for latency, cost, and quality at scale.

• Engineer 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, including model performance tracking, hallucination detection, token cost monitoring, latency profiling, and bias alerting.

• Implement responsible AI controls at the application layer, including 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 through code reviews, automated testing (unit, integration, AI evaluation), CI/CD pipelines, and documentation for AI-enabled features.

Requirements

• 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.

• 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 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-4, 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.

Qualifications

• Hands-on production experience integrating LLM APIs: OpenAI GPT-4, 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.

Skills

• React and Next.js (App Router, SSR, SSG, streaming)

• TypeScript

• Python (FastAPI preferred)

• 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

• LLM integration: OpenAI GPT-4, Anthropic Claude, Google Gemini, Meta Llama, or Mistral

• RAG architecture: document processing, embedding models, chunking strategies, semantic search, vector databases (Pinecone, Weaviate, Chroma, pgvector, Qdrant)

• Agentic AI frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or OpenAI Agents SDK

• Prompt engineering and context engineering

• Responsible AI controls: input/output guardrails, content filtering, PII redaction, rate limiting, and audit logging

• Cloud platforms: AWS, Azure, or GCP

• 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

• General observability: OpenTelemetry, Prometheus, Grafana, or Datadog

Benefits & Perks

• Great Medical/Dental Benefits

• Company-Matched 401K Retirement Savings

• Annual Bonus Program

• Educational Assistance

• Relaxed Dress Code

• PASATalks Speaker Summits

• Leadership & Mentorship Programs

• High5 Reward Recognition Program

• Onsite Happy Hours