Build and extend services, CLIs, and data pipelines — using AI agents as your primary development lever to achieve extreme iteration speed.
Adjust and enhance the existing AI-powered backporting automation to handle new data types and product requirements.
Migrate tens of thousands of entries of historical data between storage systems while maintaining compatibility with downstream tooling.
Design and implement workflows for features currently missing from the automation pipelines.
Treat iteration velocity as a first-class metric — build the way you ship: fast, measurable, and with AI doing the heavy lifting on boilerplate, tests, and scaffolding.
Drive the migration of teams and services from the legacy pipeline to the new Automation Pipeline infrastructure, ensuring zero disruption to ongoing backporting work.
Audit both old and new pipeline implementations — make sure nothing viable is lost in transit.
Onboard the new product seamlessly into the existing ecosystem, designing any missing integrations along the way.
Collaborate across units to onboard more customers and domains onto the shared benchmarking infrastructure.
Bypass the documentation tax: leverage AI to automate knowledge sharing, onboarding artifacts, and technical documentation — the team scales through high-signal artifacts, not meetings.
Share updates on delivered features effectively and asynchronously.

Bachelor's or Master's in Computer Science, Engineering, or a related field
3+ years of professional experience as a developer or engineering lead
Generalist engineering DNA — comfortable across the stack: backend services, CI/CD pipelines, data migrations, infrastructure-as-code. You pick up whatever language or tool the problem demands.
Hands-on LLM integration — experience calling LLM APIs (OpenAI, Anthropic, etc.) and familiarity with techniques like RAG, CAG, and agentic patterns
AI-native development workflow — you actively use AI coding agents (Cursor, Windsurf, aider, Claude Code, etc.) not as a novelty but as your primary mode of building software
Infrastructure skills — CI/CD (Jenkins / GitLab CI), containerization (Docker), testing automation
Experience establishing projects from scratch, including basic DevOps setup
Ownership mindset — you find bottlenecks and build the tooling to remove them, without waiting for a ticket
Cross-functional teamwork — proven ability to coordinate across multiple teams
Effective asynchronous and remote communication.

Nice-to-Haves

Proficiency in Python — including advanced topics: multithreading, async, networking, OS internals (the current codebase is predominantly Python)
Published research or experience validating engineering hypotheses through experiments
Data analysis skills — representing results, building metrics
Basic knowledge of C (ability to read a small C diff and reason about its impact)
Experience mentoring junior engineers

A strong focus on professional development with opportunities for learning and growth:
Interesting and challenging projects,
Mentor and other knowledge-exchange programs.
Fully remote work with flexible working hours, that allows you to schedule your day and work from any location worldwide.
Paid 24 days of vacation per year, 10 days of national holidays, and unlimited sick leaves to ensure you maintain a healthy work-life balance.
Compensation for private medical insurance.
Co-working and gym/sports reimbursement.
The opportunity to receive a reward for the most innovative idea that the company can patent, fostering a culture of creativity and innovation.

AI-First Generalist Engineer – Patch Backporting Infrastructure

Key skills