Additionalinformation

Additional Information

Why join Sia

• You ship. Engagements are measured in weeks, not quarters. Your work goes live in front of real users and the client’s team, with a senior colleague alongside you.
• Range without job-hopping. You’ll work across several industries and architectures each year, which is one of the fastest ways to build judgement about what actually works.
• A practice that’s growing. The AI practice is young enough that you help shape how it delivers, and experienced enough that you’re never the only person who has solved a problem before.
• Direct exposure, with support. You’ll sit in on conversations with business decision-makers early on, with senior colleagues guiding you, which is one of the fastest routes to seniority.

What we offer

• A competitive salary and benefits, including a mobility budget or company car, hospital insurance, and a performance-related bonus.
• A dedicated learning budget, funded partner certifications (Anthropic, AWS, Databricks), and paid conference time.
• Hybrid working from our Brussels or Antwerp office, with real flexibility on where the deep work happens.
• A named mentor from day one and a clear path from delivery engineer to lead engineer or client-facing architect.

We are unable to provide a work permit for this position.

Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.

Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.

Companydescription

Company Description

Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We’re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes.

Jobdescription

Job Description

Most GenAI never leaves the demo. We hire for the people who get it into production, and into daily use. The Forward Deployed Engineer closes that gap: a field engineer who builds GenAI applications (agents, RAG pipelines, copilots) and makes sure they get adopted, embedded in daily workflows, and deliver measurable value. You quickly grasp a client's operational challenges, design a tailored response, deploy it, and bring teams along in the transformation.

You will operate at the intersection of Data Science, ML/Platform Engineering, and client delivery, helping customers find the right level of sophistication, semantic search, RAG, agents, or fine-tuning, deliver measurable ROI in the most cost-effective way. Beyond crafting prompts, you will design and build robust, scalable products: benchmarking candidate Foundation Models across the model stack (from GPT-x, Claude, and Gemini to edge models like Mistral), rapidly iterating prototypes, and validating ideas directly with users using frameworks such as LangChain, LlamaIndex, and pgvector.

We work closely with leading technology partners including Anthropic (Claude), OpenAI, Databricks, and AWS. In practice that means early access to new model releases, direct engineering channels to the labs, funded certifications, and the freedom to pick the right tool, not just the one you're locked into.

What you'll actually do

• Spend time on client sites with a senior Forward Deployed Engineer, learning how their teams work and where AI can help.
• Help scope and build LLM-based solutions under guidance: RAG pipelines, tool-using agents, workflow automation, and chat interfaces.
• Connect these solutions to existing systems (APIs, databases, business tools) using established patterns and templates.
• Help apply data protection and AI regulation requirements (GDPR, EU AI Act, model licences) to your work, following the guidance your senior colleagues set.
• Support user adoption: prepare documentation, contribute to training sessions, and collect feedback on what works and what doesn’t.
• Measure how solutions perform in use, and suggest improvements to your senior colleagues.
• Share progress in clear written and verbal updates to both technical and business audiences.

Qualifications

You’ll thrive here if

• You hold a Master’s degree in computer science, AI, or a related technical field. What matters most is that you can build things, so strong projects alongside your degree count just as much.
• You have 0 to 2 years of experience, whether professional, from internships, or from a research or academic setting. We hire for potential, so recent graduates with solid LLM projects are encouraged to apply.
• You’ve built with LLMs in some form, through coursework, your thesis, internships, or personal projects. That includes prompting, RAG, basic agent workflows, and checking model outputs with simple evaluation sets or tests.
• You write Python that’s clean enough for a colleague to read, and you’re willing to learn whatever language a client’s stack requires.
• You’re comfortable calling APIs and connecting different systems, even if you’ve done it mainly in side projects.
• You’re starting to understand what makes a result useful to a client, and you can prioritise tasks with guidance.
• You enjoy explaining technical ideas to non-technical people, and you listen carefully so you can bring them along.
• You want to grow quickly, take on more responsibility over time, and learn from experienced colleagues in a fast-growing AI practice.
• You speak English and French, and ideally Dutch. German is a strong advantage. If you’re not there yet, we’ll support you in learning it.

Bonus points for

• Experience with applied AI frameworks or SDKs (LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, etc.).
• Experience deploying a project to a cloud environment (AWS, Azure, or GCP), even a small one.
• Familiarity with MLOps or LLMOps practices, such as version control for prompts, simple evaluation pipelines, or basic monitoring.
• A working understanding of vector databases and core concepts like embeddings.