AI Systems Engineer (Sagacify)
At a glance
Mid-level AI Engineering role at Craftzing. Antwerp · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build LLM agents and conversational systems that plan tasks and use external tools or APIs
- Connect enterprise data to LLMs through RAG pipelines, then improve answer quality and reduce hallucinations
- Track system quality in production, including latency, API costs, bias, drift and quality metrics
- Automate model releases with CI/CD and testing, while managing versions, reproducibility and safe rollouts
- Keep production models and services reliable by updating dependencies and working with engineering and infrastructure teams
What you bring
- You have three to five years in AI/ML engineering or a related field
- You understand LLM foundations, including Transformers, attention, generation settings and fine-tuning
- You can communicate fluently in Dutch and English; French is also listed as an option alongside those languages
Who this fits
This mid-level role suits someone who wants hands-on work across AI engineering and MLOps, with most time spent building AI systems. The work is based in Antwerp with a hybrid setup. You’ll report to the Head of ML, collaborate across teams and may grow toward a Team Lead role over time.
From the employer
Craftzing and Sagacify are looking for a Mid-Level AI Systems Engineer who is equally at home shipping an agent to production as debugging why it drifted three weeks later. Someone who does not see building AI systems and operating them as a trade-off, but as two things that make each other better.
Do you get energy from designing LLM-powered agents and RAG pipelines while staying just as sharp on monitoring, CI/CD and safe rollouts once they're live? Then you will probably feel right at home here.
We're on the lookout for a Mid-Level AI Systems Engineer who bridges AI engineering and MLOps. Someone who loves getting hands-on with LLMs, agents, and RAG pipelines, and who cares just as much about what happens once a model reaches production as about building it in the first place. In this role, you'll design, build, deploy, and operate production-grade AI systems, with a strong focus on generative AI, LLM-based applications, and reliable machine learning operations. In practice your time is split roughly between 60-70% AI Engineering and 30-40% MLOps.
In this role you sit at the intersection of engineering and operations, working across teams and disciplines. You'll report to the Head of ML at Sagacify and collaborate with the wider Sagacify and Craftzing delivery organisation. A role that can naturally grow towards a Team Lead position over time.
What you'll do
AI Engineering (60-70%)
Orchestrate AI system components: LLMs, vector databases, APIs, orchestration layers, and user interfaces
Develop autonomous agents and conversational systems that can plan actions and interact with external tools or APIs
Build and optimise RAG pipelines connecting enterprise data sources to LLMs for grounded, contextualised, reliable answers
Evaluate system quality through generative AI metrics, coherence tests, and production monitoring (latency, API costs, bias)
Deploy and scale solutions with strong attention to latency, security, reliability and cost efficiency
Keep an eye on the ecosystem for new models, frameworks and techniques, including open-source tools such as LangChain, LangGraph, Langfuse, etc.
Perform prompt and context engineering to improve output quality, reduce hallucinations, and manage conversational state effectively
MLOps (30-40%)
Build and maintain automation for model deployment, including CI/CD pipelines and automated testing
Continuously monitor model performance in production, including drift detection and quality metric tracking
Manage updates of libraries, models, and related dependencies in production environments
Ensure versioning, reproducibility and safe rollout of models and AI services
Collaborate closely with ML engineers, developers, DevOps and infrastructure teams for smooth delivery
Stay current with the latest MLOps practices, tools and platform components
You stay in the code, you stay curious about what happens after deployment, and you keep learning as you go.
What you'll work with
Core technologies :
Python
OpenAI API, Hugging Face Transformers
LangChain, LangGraph, Langfuse
FastAPI or Flask, Docker, Kubernetes, CI/CD pipelines
Cloud GPU
AWS (S3, SQS, IAM, RDS, etc.)
Cursor
Bonus points for experience with :
TypeScript
Azure
AI-native engineering workflows: hands-on experience using AI coding agents and AI-assisted development environments (Cursor, Claude Code, Windsurf, Copilot, etc.), including context engineering, subagent orchestra…
Who we're looking for
You have 3 to 5 years of experience in AI/ML engineering or related fields
You have a solid understanding of LLM fundamentals: Transformers, attention mechanisms, generation parameters and fine-tuning approaches
You have strong problem-solving ability and algorithmic creativity
You communicate clearly with both technical and non-technical stakeholders
You have a team spirit and enjoy collaborating across multiple roles
You bring rigour, responsiveness and a good incident-handling mindset
You're autonomous, curious, and quick to learn new tools
You can communicate fluently in Dutch, English and French, or at least the first two languages
Don’t worry if you don’t tick every single box, what matters most is the right mindset and a drive to learn. If you think we’re a match, we’d love to hear from you.
Why you'll love working here
Sagacify is part of Craftzing, and together we build AI solutions that create lasting impact for our clients. You will be part of a team of enthusiasts who love to learn, continuously develop new skills and fast-track our customers' AI journey across various industries.
At Craftzing and Sagacify, you get the trust and space to do your best work - in a human-sized environment with real team spirit, flexible working and room to take ownership.