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Empiric · 6 hours ago

AI Engineer/Architect

United States

Full-time

Remote

Senior Level

$150K/yr - $180K/yr

5+ years exp

Empiric is a company that designs and delivers bespoke AI solutions combining state-of-the-art models with robust engineering. They are seeking a hands-on builder who can push the boundaries of AI while applying strong engineering principles.

Cyber SecurityDatabaseInformation Technology

Responsibilities

Design, prototype, and scale AI-native applications and agent-based systems that drive real business outcomes

Work end-to-end across the stack, including front-end development (React, TypeScript), backend services (Python, Node.js, Go), APIs, and data stores (SQL, NoSQL, and vector databases)

Build and optimize LLM-driven workflows, leveraging techniques such as retrieval-augmented generation (RAG), embeddings, multi-agent orchestration, and effective context management

Architect, deploy, and maintain infrastructure, including CI/CD pipelines, Kubernetes, cloud services, and observability tooling

Move efficiently from proof-of-concept to production, balancing speed with scalability, security, and long-term maintainability

Continuously optimize AI systems for accuracy, performance, latency, and cost efficiency

Partner closely with customers, engineers, product managers, and designers to translate experimentation into reliable, production-ready features

Stay hands-on with modern, developer-first tools such as Cursor, Claude Code, GitHub Copilot, and similar platforms to maximize productivity

Qualification

AI-powered systemsFull-stack developmentCloud-native architecturesLLM development toolsKubernetesCI/CD workflowsCuriosityEnthusiasmCommunicationCollaboration skills

Required

5+ years of professional software engineering experience, including at least 2 years building AI-powered systems

Strong full-stack background, with experience in modern front-end frameworks (React, TypeScript), backend development (Python, Node.js, Go), and a range of databases (SQL, NoSQL, vector stores)

Familiarity with AI and LLM development tools such as Cursor, Claude Code, GitHub Copilot, LangChain, CrewAI, or comparable frameworks

Hands-on experience with cloud-native architectures (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD workflows, monitoring, and scalable systems

Solid understanding of the LLM lifecycle, including prompting strategies, evaluation, fine-tuning, embeddings, RAG, and agent design

A pragmatic engineering mindset—you recognize that reliable AI systems require testing, observability, safeguards, and fallback logic, not just clever prompts

Strong communication and collaboration skills, with the ability to bridge technical depth and business context

Curiosity and enthusiasm for exploring new ideas, paired with a commitment to delivering production-quality software