Additionalinformation

Additional Information

Why join Sia

• You ship. Engagements are measured in weeks, not quarters, and your work goes live in front of real users, with the client's team, not behind a glass wall.

• Range without job-hopping. You will touch several industries and several architectures a year, which is the fastest way to build judgement about what actually works.

• A practice that is growing, not maintaining. The AI practice is young enough that you shape how it delivers, and senior enough that you are never the only person who has solved the problem before.

• Direct exposure. You sit with business decision-makers from day one, the fastest route to seniority we know.

What we offer

• A competitive salary with great advantages, including hospital insurance and performance -related bonus.

• A dedicated learning budget, funded partner certifications (Anthropic, AWS, Databricks), and paid conference time.

• Hybrid working from our Luxembourg office, with real flexibility on where the deep work happens.

• A mentor from day one and a clear path from delivery 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

• Sit with a client's team, work out where AI actually earns its keep (and where it doesn't), and turn that into something you can ship.

• Design and implement LLM-based systems: autonomous agents, RAG pipelines, workflow automation, and conversational interfaces.

• Integrate these solutions into clients' existing environments (APIs, databases, business tools), complying with applicable regulations (GDPR, EU AI Act, model licenses, etc.).

• Get people to actually use what you built: training, winning sceptics over, and iterating until it sticks rather than sits.

• Iterate quickly with stakeholders to refine solutions in production and maximize their impact.

• Be the person in the room who can talk to the engineers and to the board, and be believed by both.

Qualifications

You'll thrive here if

• Have a grounding in computer science, AI, or a related technical field. A degree is the usual route, but we care that you can build.

• 2+ years of professional experience. We hire for trajectory, so strong graduates with serious LLM projects behind them are genuinely encouraged to apply

• Have hands-on experience building with LLMs, whether in production, in research, or in substantial personal projects: advanced prompting, RAG, fine-tuning, agent orchestration, and systematic evaluation of model outputs (evaluation sets, regression testing).

• Write Python you'd be happy to hand to someone else and can pick up whatever language the client's stack demands.

• Are comfortable integrating APIs and connecting heterogeneous systems.

• Have a strong sense of client context: you identify what's genuinely useful, prioritize, and deliver value quickly.

• Can bring non-technical teams along in a transformation, with teaching ability, active listening, and an ability to communicate ideas across a wide range of expertise.

• Want to quickly take on responsibility for high-value engagements, within a dynamic, fast-growing AI practice.

• Are fluent in French and English. German is a nice advantage, and we support you in learning it if you are not there yet.

Bonus points for

• Experience with applied AI frameworks (LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, etc.).

• Experience deploying and operating solutions in a cloud environment (AWS, Azure, or GCP).

• Exposure to MLOps/LLMOps practices (CI/CD for models, prompt versioning, evaluation pipelines, observability).

• Strong understanding of vector databases and related concepts (e.g., embeddings, neural networks, latent space).