Founding AI Engineer

Important: We do not accept standard one-click applications. To be considered, you must follow the application instructions provided at the end of this posting, including submission of project links and responses to the screening questions. Applications that do not meet these requirements will not be reviewed. Role Overview Aeris Dynamics is not a software company, we build and operate real-world systems. This role exists to embed AI directly into those operations. We are seeking a Founding AI Engineer to serve as the first dedicated software engineering hire at Aeris Dynamics. This role will anchor a new digital products function within our Technical Department. You will lead the development and scaling of two core platforms: Cold Chain Intelligence (CCI) Aeris Control Tower (ACT) In parallel, you will implement Generative AI (GenAI) solutions across the enterprise, embedding AI into real workflows to improve productivity and unlock new value. This is a hands-on, production-focused role. You will take AI-driven prototypes into fully deployed systems, establish engineering standards, and work closely with domain experts and external partners. Key Responsibilities Design, build, and deploy GenAI features (classification, extraction, summarization, drafting, agent workflows) Embed AI into live business workflows with proper evaluation, observability, and guardrails Architect and develop scalable full-stack systems for CCI and ACT Convert AI prototypes into secure, production-grade applications Own Google Cloud infrastructure (Cloud Run, Cloud SQL, Firebase), including deployment, CI/CD, scaling, security, and cost Integrate third-party APIs (IoT, logistics, weather, geolocation, messaging) reliably at scale Provide technical oversight of external development partners and uphold engineering standards Build and manage data pipelines, analytics systems, and ML-driven improvements Leverage AI coding tools to accelerate delivery while maintaining engineering quality Requirements Must-Have Proven experience deploying GenAI/LLM-powered features in production (e.g. RAG, copilots, agents, classification) 2–3 years of full-stack development experience (frontend, backend, data, deployment) Hands-on cloud experience with ownership of infrastructure Experience with Google Cloud Platform (Cloud Run, Cloud SQL, Firebase) Proficiency with AI-assisted coding tools (e.g. Cursor, Codex, Claude Code) Strong system design and architectural judgment Ability to work independently in ambiguous, fast-moving environments Strong communication skills with both technical and non-technical stakeholders Nice-to-Have LLM engineering (RAG pipelines, evaluation, fine-tuning, agent frameworks) Data engineering and applied machine learning Python for scientific/numerical computing (NumPy, SciPy) Experience in IoT, logistics, supply chain, or data-intensive systems Screening Questions (Project-Based Assessment) Please share examples of projects that you have actively worked on or built end-to-end. You must include links (e.g. GitHub, portfolio, or deployed applications) where applicable. Your answers should be based on these projects. 1. Full-Stack Experience For this role, full-stack development experience means professionally building and shipping web applications where you personally worked across all four layers: frontend (a modern framework such as React, Vue, or Angular), backend (APIs and server-side business logic), data (schema design and production queries), and deployment (cloud infrastructure). How many years of professional experience do you have that meets this definition? Briefly describe the application you would point to as your strongest example and include a project link . 2. Production GenAI Experience Have you shipped an LLM-powered feature (e.g. triage/classification, RAG, or agents) into a production environment? If yes, briefly describe what it did, who used it, and your role in building it. If you have built prototypes but not production deployments, describe your strongest prototype instead. Include a relevant project link where available. 3. Cloud Experience Describe your hands-on cloud experience: which provider(s), which services, and what you were responsible for (deployment, CI/CD, scaling, security, cost). Please state specifically whether you have worked with Google Cloud Platform (GCP) in a deployed environment. Support your answer with project examples and links where possible . How to Apply Submit your application wi…