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 with:

Updated resume

Current and Expected Salary
Notice period

Application Requirements

As this is a founding engineering role within a non-software organization, we place strong emphasis on practical, hands-on building experience.

Your application must include:

Project Links: Active links to systems or applications you have built or contributed to (e.g. GitHub, deployed apps, portfolios)
Screening Responses: Written answers to all screening questions above, based on your own work

Applications that do not include both project links and screening responses will not be reviewed.

Additional Information:

AWS plus Variably Bonus
A genuine learning budget: including the AI tools that make you faster (e.g. Claude Max plan)
The hardware you need to do your best work.
Ground-floor ownership of products with a real commercialization roadmap, and the chance to shape an engineering team from day one.

Aeris Dynamics Pte Ltd

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