Product Engineer

We are looking for a Product Engineer who combines strong software engineering fundamentals withAI-native development and product thinking. You should be someone who can move from user problem to product decision, prototype, production, and iteration. You will build full-stack, AI-powered products, work directly with users, experiment rapidly, and have significant influence over both what we build and how we build it. This role is ideal for engineers who thrive in ambiguity, move fast without compromising engineering judgment, and genuinely enjoy building products from scratch.

Responsibilities:

• Build AI/LLM-powered products end-to-end, from backend systems and AI workflows to user-facing experiences.
• Design and implement applications using LLMs, VLMs, Voice AI/VoiceLMs, SLMs, agents, or other emerging AI models.
• Work directly with users to understand problems and translate them into product and engineering decisions.
• Rapidly prototype, validate with real users, and iterate based on feedback.
• Own features and products from architecture and implementation through deployment and production reliability.
• Evaluate new models, frameworks, tools, and agentic patterns and determine where they create genuine product value.
• Use AI-assisted development tools such as Cursor, Claude Code, GitHub Copilot, or equivalent as part of your everyday engineering workflow.
• Help establish engineering standards, architecture, quality practices, and culture as products scale beyond 01

Requirements:

• Strong Software Engineering Foundation.
• 4-8 years of professional software engineering experience.
• Strong backend and/or full-stack engineering fundamentals.
• Experience designing, building, deploying, and maintaining production applications.
• Comfortable making architecture and engineering trade-offs independently.
• Hands-on AI / LLM Building Experience.
• At least 1+ year of meaningful hands-on experience building with AI/LLMs.
• You may have worked with: LLMs / SLMs, VLMs / multimodal models, Voice AI / VoiceLMs, RAG and retrieval systems, AI agents and tool-calling workflows
• Structured outputs and AI-powered application workflows.
• We care about what you have built, not simply whether you have used an LLM API.
• You should be able to explain the problem, architecture, model choices, trade-offs, failure modes, evaluation approach, and what you learned.

AI-Native Engineering Workflow:

• You actively use tools such as Cursor, Claude Code, GitHub Copilot, or equivalent to improve your development velocity.
• More importantly, you understand their limitations- where they help, where they hallucinate, how context limitations affect implementation, and when human engineering judgment needs to take over.

01 Builder Mindset:

• You have experience building in a startup, venture, or similarly fast-moving environment where requirements were not always clearly defined.
• Own outcomes rather than tasks.
• Can turn ambiguity into an executable plan.
• Prototype and ship quickly.
• Make pragmatic technical decisions.
• Don't wait for perfect specifications before moving.

Production-Scale Experience:

• You have worked on at least one meaningful production system at scale, ideally in a Series A+ product company or an equivalent environment.

You understand:

• Reliability and failure modes
• Observability and monitoring
• Performance and scalability
• Production debugging
• Technical debt and the cost of engineering shortcuts

Strong Product Orientation:

• You think beyond How should we build this? and actively ask Should we build this at all?
• You have preferably worked in a product company and interacted closely with B2B or B2C users.
• You should be comfortable challenging product assumptions, discussing UX, prioritising what matters, and influencing what gets built next.

Curiosity About the AI Frontier:

• You actively follow developments across models, agents, developer tooling, multimodal AI, inference, and emerging AI engineering patterns.
• You should be able to discuss meaningful developments from the last few months and, more importantly, explain which ones matter for building better products and why.

Good to Have:

You are likely to thrive here if you:

• Have 4-8 years of strong software/product engineering experience.
• Have 1+ year of hands-on AI/LLM product-building experience.
• Have built and shipped at least one AI-powered product, feature, or serious project.
• Come from a product company or high-ownership startup environment.
• Have meaningful01 product-building experience.
• Have worked on a production system at meaningful scale.
• Use AI coding tools extensively and intelligently.
• Enjoy speaking with users and influencing product decisions.
• Prefer ownership and speed over rigid processes and narrowly defined responsibilities.