Generative AI Engineer

Archer•San Jose, CA

12h

About The Position

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. We are designing, manufacturing, and operating an all-electric aircraft that can carry four passengers while producing minimal noise. Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success. We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members. We are looking for a hands-on Generative AI Engineer to join our Corporate Technology group to design, build, and deploy AI-driven applications powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based systems. You will create scalable, production-ready AI solutions that enhance natural language understanding and seamlessly integrate intelligence into our product ecosystem. This role blends full-stack engineering, LLM architecture, and AI system integration—ideal for a mid-level engineer who can both design and implement features end-to-end. You will collaborate closely with product, engineering, and business teams to align technical design with real-world objectives while continually learning and evolving with the GenAI landscape.

Requirements

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.

Nice To Haves

Familiarity with LLM evaluation frameworks (RAGAS, DeepEval, custom evaluators).
Understanding of LLM safety, guardrails, hallucination mitigation, and responsible AI principles.
Experience with observability/tracing for AI workflows (latency, token analytics, vector search performance).
Background in MLOps, experiment tracking, or fine-tuning workflows.

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.