AI Developer IV

This is a fully remote position, open to applicants in Oregon.

📋 Description

• Design and develop internal AI solutions to enhance engineering productivity and streamline software delivery.

• Create AI-powered developer workflows and assistants, implement agentic SDLC automation patterns, and develop internal tools for code analysis, documentation, testing, migration, and engineering support.

• Establish reusable patterns for prompts, tool-calling, workflows, and reference implementations.

• Construct multi-step agentic workflows, tool-calling and orchestration patterns, RAG-based internal knowledge solutions, shared SDKs, templates, integration examples, and reusable components for copilots and agents.

• Collaborate with senior architects and engineering leaders to define scalable patterns.

• Engage directly with engineering teams, champions, and internal stakeholders to facilitate AI adoption.

• Work in tandem with teams on AI use cases and implementation strategies.

• Offer technical guidance on LLM, RAG, and agentic workflow design.

• Assist in proof-of-concept efforts and evolve them into sustainable practices.

• Develop playbooks, examples, templates, and documentation for internal engineering purposes.

• Participate in office hours, workshops, and AI enablement sessions.

• Help to establish and implement evaluation and governance practices for prompt and workflow assessment, including accuracy, relevance, usefulness, safety, responsible AI, PII handling, logging, observability, feedback loops, and human-in-the-loop review.

• Collaborate with engineering leadership, product teams, architecture, security, and other stakeholders to identify and deliver impactful AI use cases.

• Transform engineering productivity requirements into AI-enabled solutions and support AI initiatives aligned with the roadmap.

• Contribute to goals for adoption and capacity improvement while measuring the impact of AI enablement.

• Clearly communicate technical concepts to both engineering and non-engineering audiences.

• Design AI solutions considering model selection and routing, prompt and context optimization, caching and retrieval efficiency, latency, reliability, build-vs-buy decisions, and vendor lock-in factors.

⛳️ Requirements

• Generally, 6+ years of software engineering experience, including substantial hands-on experience in building production applications or internal platforms.

• At least 2+ years of experience in applied AI, LLM, Generative AI, or agentic workflows.

• Strong programming skills in Python, TypeScript/JavaScript, or similar production languages.

• Experience in designing and developing cloud-native applications or services on Azure, GCP, or AWS.

• Practical knowledge of LLM-based application development, prompt engineering and versioning, tool calling/function calling, RAG architectures, vector databases or semantic retrieval, and multi-step workflow or agent orchestration.

• Familiarity with CI/CD, Git-based development, automated testing, API design, observability, and logging.

• Experience using or implementing AI coding tools such as GitHub Copilot, Cursor, Windsurf, Codex, or similar tools.

• Capability to work directly with engineering teams to grasp needs, prototype solutions, and promote adoption.

• Strong verbal and written communication skills, with the ability to articulate AI concepts and implementation patterns clearly.

• Nice-to-have: Background in building internal developer platforms, engineering productivity tools, or enablement frameworks.

• Nice-to-have: Familiarity with agent frameworks or orchestration tools such as LangGraph, OpenAI Agents SDK, Google ADK, Semantic Kernel, CrewAI, or similar frameworks.

• Nice-to-have: Knowledge of evaluation frameworks such as OpenAI Evals, LangSmith Evals, RAGAS, or custom evaluation harnesses.

• Nice-to-have: Experience with browser automation or workflow automation tools like Playwright.

• Nice-to-have: Familiarity with knowledge management, internal documentation systems, or enterprise search.

• Nice-to-have: Experience working in environments considering privacy, compliance, or regulated-data issues.

• Nice-to-have: Background in enterprise software, PropTech, fintech, or other complex business sectors.

• Nice-to-have: Experience supporting AI adoption initiatives, engineering champions, office hours, or internal technical enablement.

🏝️ Benefits

• Health, dental, and vision insurance.

• Retirement savings plan with company matching.

• Paid time off and holidays.

• Opportunities for professional development.

• Performance-based bonuses relative to position.

• Additional rewards such as annual bonuses and sales incentives, depending on the applicable plan, role, and individual performance.