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Archer · 1 day ago

Generative AI Engineer

San Jose, CA

Full-time

Onsite

Mid, Senior Level

$150K/yr - $200K/yr

7+ years exp

Archer is an aerospace company based in San Jose, California building an all-electric vertical takeoff and landing aircraft with a mission to advance the benefits of sustainable air mobility. They are seeking a hands-on Generative AI Engineer to design, build, and deploy AI-driven applications powered by Large Language Models and agent-based systems, enhancing natural language understanding within their product ecosystem.

AerospaceAir TransportationElectric VehicleManufacturing

No H1B

Responsibilities

Develop, deploy, and maintain LLM-powered applications supporting natural language understanding, conversational search, and intelligent automation

Build and optimize RAG pipelines, including embeddings, chunking strategies, retrieval logic, ranking, evaluation, and continuous improvement loops

Design and implement agent-based workflows for reasoning, tool usage, routing, and structured task execution

Apply advanced prompt engineering techniques—including chain prompting, few-shot prompting, and structured prompting—to improve LLM reliability and output quality

Architect and implement high-performance applications and scalable backend systems that incorporate AI capabilities into diverse solutions

Build full-stack features using React (or similar), Node.js/Python, REST/GraphQL APIs, and modern backend patterns

Deploy and operate AI applications in cloud environments (AWS, Azure, GCP) using Docker, Kubernetes, and CI/CD tooling

Ensure system-level reliability with robust monitoring, observability, telemetry, and performance metrics for LLM workloads

Partner with product managers, business teams, and engineers to align AI initiatives with business priorities and user needs

Translate technical concepts through clear presentations, design reviews, and documentation, ensuring shared understanding across teams

Participate in scoping, requirements definition, and architectural discussions, influencing design decisions and system roadmaps

Implement evaluation frameworks for RAG, agents, and LLM responses (latency, retrieval accuracy, hallucination detection, etc.)

Continuously refine data quality, retrieval accuracy, indexing strategies, and model selection processes

Drive process improvements across the AI development lifecycle to enhance system robustness and engineering productivity

Diagnose and resolve issues across retrieval layers, LLM integration, application logic, and infrastructure

Qualification

Generative AI applicationsLLM architectureRAG pipelinesFull-stack engineeringOpenAI GPTDockerKubernetesCI/CD pipelinesPrompt engineeringCuriosityCommunicationProblem-solving

Required

7–8 years of full-stack engineering experience building scalable, cloud-native applications

2-4+ years of hands-on experience designing and deploying Generative AI applications or LLM-powered systems

Practical experience with OpenAI GPT, Azure OpenAI, AWS Bedrock, Gemini, Vertex AI, or similar LLM ecosystems

Strong background designing and optimizing RAG architectures and retrieval workflows

Experience with at least one orchestration or agent framework, such as: LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, DSPy, or custom orchestration pipelines

Experience with vector databases, such as Pinecone, Weaviate, FAISS, Milvus, or similar technologies

Experience implementing and evaluating embedding models and retrieval pipelines (dense, hybrid, cross-encoder)

Proficiency with prompt engineering, including chain prompting, iterative refinement, and structured output patterns

Strong frontend development, and backend experience with Node.js/Python, REST APIs, SQL/NoSQL databases

Deployment experience with Docker, Kubernetes, and CI/CD pipelines across AWS, Azure, or GCP