Full-Stack AI Engineer

CHILI publish NV; Oost-Vlaanderen; Sur place; EUR 39 000 - 56 000; Plein temps

Résumé du poste

CHILI publish NV is building a cloud platform that enables brands and agencies to create, automate, and scale digital content with AI-powered features. You will work on AI systems, backend engineering, and product to turn ideas into features used by customers daily, combining coding agents with product decisions.

You’ll collaborate closely with product management, engineers and designers, owning end-to-end delivery from ideation to production, and ensuring reliability, latency, and observability

Qualifications

• 5+ years of professional software engineering experience, including 1–2 years building and operating agentic or tool-calling production systems.

Responsabilités

• Turn ideas into AI features used daily by customers across the platform.
• Build evals and benchmarks to prove agent improvements with measurable evidence.
• Develop retrieval and data pipelines for grounded agents (vector search, embeddings, ingestion).
• Own production services: streaming APIs, stateless, highly available, latency and cost considerations, logging and tracing.
• Collaborate with Product and Design to translate fuzzy intent into shipped behavior.
• Work with agents to decompose goals into executable steps and verify results.
• Stay current with evolving AI landscape and contribute investment rationale.

Connaissances

Experience with agentic systems

Non-deterministic systems evaluation

Full-stack API design

LLM ecosystem expertise

Production services

Clear communication

Outils

pgvector

Qdrant

Weaviate

Pinecone

Azure

AWS

GCP

Description du poste

We're looking for someone who'd rather build the eval that proves an agent got better than argue about vibes

At CHILI publish, we build a cloud platform that helps brands and agencies create, automate, and scale digital content with ease. AI is becoming a key driver on our platform, powering intelligent content automation, smart templating, and the agentic systems our customers will rely on next. We're not hiring for a checkbox: we're looking for someone who wants to shape how AI gets built into a real product, used by real people, at scale, working closely with your team and directly with product management.

What you'll be doing

You'll work at the intersection of AI systems, backend engineering, and product: turning ideas and sketch requirements into AI features our customers use every day, working the way we expect the whole industry to work soon: with coding agents doing a lot of the typing, and engineers doing the deciding and the verification.

• Treat prompts, tool catalogs and skills as engineered product surfaces. They're versioned, reviewed, tested and tuned like any other code we ship.
• Make quality measurable. Build the evals and benchmarks that prove a change made the agent better. Non-determinism is the normal case here; your job is to turn "this feels better" into evidence humans know "it is better."
• Build the retrieval and data plumbing that keeps agents grounded. Vector search, embedding and ingest pipelines, and the deployment work that keeps them running.
• Own it in production. Streaming APIs, stateless and highly available services, latency and token budgets, and the structured logging and tracing that backs it up.
• Turn fuzzy intent into shipped behaviour. You'll work directly with Product and Design, argue about what the agent should do in the cases nobody specified, write the ticket yourself, and ship it.
• Build with agents, not just for them. A large share of the code here is written by agents. That shifts where your day goes: decomposing a fuzzy goal into something an agent can actually execute, then verifying the result against the running system.
• Stay current with a landscape that moves weekly, and bring well-considered opinions on where we should invest next, including where we shouldn't.

Who you are

• You're product-minded. You judge the agent's output the way a user would, not just the way a test suite does. "Technically correct" and "actually good" are two different questions, and you hold both at once.
• You're comfortable with ambiguity. Requirements reach you as a screenshot, a hunch, or a complaint. You're happy to make the call, but you make your assumptions explicit before you build, and you ask the one question that unblocks you rather than guessing quietly.
• You think outside the box. When the obvious approach is brittle, you find the one that isn't.
• You take ownership end to end. From how a request is interpreted through what happens when it fails in production, you don't wait to be told something's broken — you notice, you fix it, and you're accountable for what you ship, whether you wrote it yourself or an agent did.
• You trust evidence over assumption. Docs, comments and tickets describe intent; the running system is the truth. You verify against it, and you say so plainly when the two disagree.
• Less is more. You'd rather delete than add, and you'd rather ship something small and clear than something clever nobody can maintain.
• You thrive in a collaborative, international team driven by curiosity where English is the working language and diverse perspectives are genuinely valued.

Talents you possess

Must-haves

• 5+ years of professional software engineering experience, including at least 1–2 years building and operating agentic or tool-calling production systems, with attention to their reliability, latency, observability and cost characteristics.
• Demonstrated ability to evaluate non-deterministic systems: designing test sets, benchmarks and automated graders, and distinguishing a genuine regression from grader noise.
• Full-stack capability across the request path: you can design the API, integrate it with a frontend, and apply enough product and UX judgement to make the result intuitive for the people using it.
• A practical command of the LLM ecosystem: prompt and context engineering, model selection and routing across providers, embeddings and retrieval, caching and tiering, and an informed view on when fine-tuning is and is not warranted.
• Experience operating services in production: containerised deployment, readiness and warmup semantics, structured logging and tracing, and CI/CD.
• The ability to communicate clearly: with engineers and non-engineers alike, and to record decisions in writing so that others can act on them.

Nice-to-haves

• Exposure to content generation, creative tooling, digital asset management or marketing technology.
• Cloud-native infrastructure experience (Azure, AWS or GCP; Kubernetes; Cloudflare Workers).
• Experience with retrieval systems backed by a vector database (pgvector, Qdrant, Weaviate, Pinecone or similar), including embedding pipelines and index maintenance.
• Experience serving computer-vision or other non-LLM models in production — ONNX Runtime, YOLO, OpenCV or comparable — including artifact pinning, verification and warmup.
• Some exposure to data science or applied ML research is a plus, but not something we're specifically screening for.

How we work

We're an international team headquartered in Aalst, Belgium. Our way of working is highly collaborative — short, fast increments, tight loops between engineering, product and even early-adopter clients. That rhythm works best in person, so we're looking for someone who can be in our Aalst HQ at least 2 days a week. For that reason, we're specifically looking for candidates based in Belgium or able to commit to that regular on-site presence.

You'll be joining a small, high-leverage team: a hands-on AI lead, an AI product manager, and a CPO who are directly and closely involved in shaping our agentic product features. There's no bureaucracy between a good idea and shipping it — you'll be in the room where these decisions get made, not several layers removed from them.

The gross monthly salary for this role is €3,500–€5,000. The offer within this range will depend on the candidate’s relevant experience, skills and demonstrated ability to meet the role’s requirements. We’ll discuss the salary and benefits package during the first screening call.

What to expect

An initial screening with HR on job and cultural fit, a technical/team interview with our AI lead, and a final conversation with our VP of Delivery.