AI Infra Engineer

Why This Role Exists

AI has shifted what infrastructure engineers do. The job is no longer just deploying models — it’s understanding how entire systems connect and where AI can be leveraged to automate business workflows end-to-end. The best AI infra engineers think in systems, not services. They see the full picture: model selection, agent orchestration, business automation, and the infrastructure that ties it all together.

The startups we work with need engineers who can make AI systems run reliably in production. Not research. Not notebooks. Infrastructure that serves real users, handles real data, and doesn’t break at scale. You’d work directly with founding teams on problems like:

• Agent orchestration — multi-step AI workflows with tool use, memory, and error handling
• Model serving pipelines that handle multiple frameworks and versions
• Business automation — connecting AI capabilities to real operational workflows
• Data pipelines for training, fine-tuning, and annotation workflows
• ML platform tooling — experiment tracking, model registries, deployment automation

What You’d Work On

Engagements vary by client, but the pattern is consistent: take ML systems from “works on my laptop” to “runs in production.” Recent and current projects include:

Who We’re Looking For

You build AI systems, not AI models. You think in systems — understanding how workflows connect, where AI creates leverage, and what infrastructure makes it all reliable.

• Systems thinking — you see the full picture. How data flows, where automation applies, what breaks at scale. Not just individual services.
• Strong systems Python — async, packaging, testing, profiling. Not notebooks.
• Agent and orchestration experience — you’ve built multi-step AI workflows, tool-using agents, or complex pipeline orchestration (Temporal, Airflow, or similar).
• Production AI experience — you’ve deployed models or agents that real users depend on. Model serving, training pipelines, or evaluation infrastructure.
• Infrastructure fundamentals — containers, Kubernetes, IaC. You can set up a deployment pipeline, not just use one.
• Comfortable with ambiguity — these are early-stage companies. Problems don’t have textbook answers. You scope, propose, and build.
• AI-first workflow — you use Claude Code, Cursor, or similar to move fast, but you understand what you’re building.

Bonus: domain experience in healthcare/DICOM, computer vision, or semiconductor/hardware is a strong differentiator.

What Worca Offers

• Interesting work — you’d work directly with founding teams at AI startups, not through layers of management
• Flexibility — hourly, part-time, or full-time engagements depending on the project and your availability
• USD compensation — competitive rates benchmarked to the role, not your geography
• Continuity — Worca manages employment, payroll, and compliance. When one engagement ends, we match you to the next.
• Community — you’d join a network of top APAC engineers working on cutting-edge AI problems

Engagement Structure

• Type: Contract (hourly or monthly) or full-time — depends on the engagement
• Trial: Most engagements start with a 2-4 week trial project
• Timezone: APAC-based, comfortable working US hours (overlap required)
• Location: Remote — Philippines, Taiwan, Singapore, or broader APAC

How to Apply

Send your resume and a brief note on the most interesting ML infrastructure problem you’ve solved to careers@worca.io. Include links to relevant work (GitHub, blog posts, system designs) if available.

We review every application. If there’s a fit, we’ll set up a technical conversation within a week.