We're assembling a founding team of exceptional AI engineers. Small team, real clients, hard problems. Remote-friendly. You'll build things that go to production, not demos.
Aevon.ai is early. The first 10 engineers will shape how we build, how we deliver, and what we stand for. You'll have equity, autonomy, and a direct line to clients. If you want to be part of something from the ground up — this is it.
Every engagement ships to production. We don't do POCs that die in a drawer. Your work will be running in real enterprise environments within weeks.
We work at the infrastructure and model layer — GPU clusters, fine-tuning, inference optimisation, eval frameworks. Not prompt engineering wrappers.
You'll work directly with Series B–D US tech companies. Client visibility from day one. Your name on the deliverable.
Early team members receive meaningful equity stakes with standard vesting. We're building toward a real outcome — and you'll share in it.
We work in pods, move fast, and communicate with clarity. Remote-friendly for the right people — we care about output, not office hours. We don't believe in performative presence.
You're not a ticket-taker. Each pod has genuine ownership over architecture decisions, delivery timelines, and quality standards. We hire people who can lead, then let them lead.
We care about well-designed systems, clean evaluation frameworks, and honest technical recommendations. We don't cut corners to ship fast. Quality is how we retain clients.
Early in your career at Aevon.ai, you'll touch more of the AI stack than you would in 3 years at a large company. Platform partners, enterprise clients, hard infrastructure problems.
All roles are India-based with remote flexibility. We are building the founding engineering team and moving quickly.
This is the most critical hire. You will be the technical backbone of Aevon.ai's first delivery pod. You own the architecture, run technical discovery with US enterprise clients, and set quality standards for every engineer we hire after you.
You are the hands. You build the RAG pipelines, run the fine-tuning jobs, design the eval frameworks, and optimise inference until latency and cost targets are met. Production-grade LLM engineering, not demos.
You own the infrastructure layer. GPU clusters, Kubernetes orchestration, CI/CD pipelines for ML workloads, cost monitoring, and the platform integrations that make the whole stack run reliably at scale.
You are the bridge between the client's business problem and Aevon.ai's technical capability. You run discovery workshops, write the technical sections of proposals, and own client success through delivery. Half engineer, half trusted advisor.
We're building fast. If you're exceptional at something we haven't listed, we want to hear from you.
We respect your time. The entire process takes 7–10 days from first contact to offer.
Send your CV and a note on what you've built to [email protected]