Applied Research AI Engineer

Faktion | AI Solutions & Advisory; Antwerpen; Hybride; EUR 70 000 - 110 000; Plein temps

Avantages offerts par ce poste

Company car or mobility budget

hospitalization and group insurance

top-tier laptop and smartphone

flexible hybrid working policy

Résumé du poste

Faktion | AI Solutions & Advisory is seeking an Applied Research AI Engineer to translate state-of-the-art AI into scalable customer solutions. You’ll work at the nexus of research and engineering to prototype and productionize RAG pipelines, copilots, and agentic workflows.

You will explore LLMs, retrieval systems, multimodal AI, evaluation, and fine-tuning while collaborating with engineers, PMs, and customers to deliver impact across industries.

Qualifications

• Master's or PhD in Computer Science, Artificial Intelligence, ML, NLP or related quantitative field.
• Strong hands-on experience in applied AI, ML, NLP, or generative AI with both experimentation and implementation.
• Excellent Python skills and experience with modern ML/LLM frameworks such as PyTorch, Hugging Face, LangGraph, LangChain.

Responsabilités

• Investigating the latest advancements in generative AI, ML, and applied research, and assessing how they create value for customers.
• Designing, implementing, and benchmarking AI systems such as RAG pipelines, copilots, agentic workflows, evaluation frameworks, and fine-tuned models.
• Translating research ideas, papers, and experiments into robust prototypes and production-ready components.
• Building reproducible experimentation pipelines for model evaluation, prompt optimization, dataset curation, and system comparison.
• Collaborating with AI engineers, full-stack developers, and project managers to define technical approaches and integrate research outcomes into customer solutions.
• Improving model quality, reliability, latency, and cost-efficiency through systematic experimentation and evaluation.
• Developing and maintaining containerized AI back-ends and research tooling using Python, Docker, and FastAPI.
• Creating clear documentation and technical communication around experiments, findings, trade-offs, and implementation decisions.
• Contributing to internal best practices around evaluation-driven development, experimentation, and applied AI research.
• Sharing knowledge with the team through technical mentorship, internal demos, and research reviews.

Connaissances

Python

PyTorch

Hugging Face

LangGraph

LangChain

LLMs

NLP

Generative AI

Evaluation frameworks

Docker

FastAPI

API design

Experimentation

Azure

Cloud platforms

Formation

Master's or PhD in Computer Science / AI / ML / NLP or related field

Outils

PyTorch

Hugging Face

LangGraph

LangChain

Docker

FastAPI

Description du poste

As an Applied Research AI Engineer, you will help turn the latest advances in AI into practical, scalable solutions for our customers. You will sit at the intersection of research and engineering: investigating new models, evaluation methods, and system designs, then translating them into prototypes and production-ready building blocks.

We are looking for a highly motivated Applied Research AI Engineer to join our AI engineering team. In this role, you will explore state-of-the-art techniques in areas such as LLMs, retrieval systems, agentic workflows, multimodal AI, evaluation, and fine-tuning, and work closely with engineers, project managers, and customers to bring those innovations into real-world use cases.

Key Responsibilities

• Investigating the latest advancements in generative AI, machine learning, and applied research, and assessing how they can create value for our customers.
• Designing, implementing, and benchmarking AI systems such as RAG pipelines, copilots, agentic workflows, evaluation frameworks, and fine-tuned models.
• Translating research ideas, papers, and experiments into robust prototypes and production-ready components.
• Building reproducible experimentation pipelines for model evaluation, prompt optimization, dataset curation, and system comparison.
• Collaborating with AI engineers, full-stack developers, and project managers to define technical approaches and integrate research outcomes into customer solutions.
• Improving model quality, reliability, latency, and cost-efficiency through systematic experimentation and evaluation.
• Developing and maintaining containerized AI back-ends and research tooling using technologies such as Python, Docker, and FastAPI.
• Creating clear documentation and technical communication around experiments, findings, trade-offs, and implementation decisions.
• Contributing to internal best practices around evaluation-driven development, experimentation, and applied AI research.
• Sharing knowledge with the team through technical mentorship, internal demos, and research reviews.
• Master's or Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a related quantitative field.
• Strong hands-on experience in applied AI, machine learning, NLP, or generative AI, ideally in both experimentation and implementation.
• Excellent Python skills and experience with modern ML and LLM frameworks such as PyTorch, Hugging Face, LangGraph, LangChain, or similar tooling.
• Solid understanding of transformer models, embeddings, fine-tuning, prompt engineering, and evaluation methodologies.
• Experience designing experiments and interpreting results in a rigorous, pragmatic way.
• Ability to move from research concepts to working prototypes and production-oriented solutions.
• Strong understanding of software engineering fundamentals, data structures, algorithms, and version-controlled development workflows.
• Familiarity with cloud platforms and AI services such as Azure.
• Strong communication skills and the ability to explain complex technical ideas to both technical and non-technical stakeholders.
• A proactive and curious mindset: you like exploring new ideas, but you also know how to focus on what works in practice.
• Fluent in English and Dutch. French is a plus.

Nice to Have

• Publications in relevant AI/ML conferences and journals.
• Open-source contributions, research repos, or public projects demonstrating strong applied AI work.
• Experience with LLM evaluation, observability, and benchmarking frameworks.
• Experience with multimodal systems, synthetic data generation, or post-training methods.
• Experience deploying AI solutions in enterprise environments.
• Experience in one of our focus domains: Data Quality, Retail, Manufacturing, Finance.
• Experience working in consulting, customer-facing delivery, or cross-functional product teams.

We Offer

• A rewarding salary package that includes additional perks like a company car and fuel card or a mobility budget, comprehensive hospitalization, group insurance, and a top-tier laptop and smartphone.
• A company culture that stimulates both individual and team development, fostering professional growth.
• The opportunity to work on meaningful, innovative AI projects that bridge research and real-world impact.
• Time and space to investigate new tools, frameworks, and methods that can strengthen our technical offering.
• Regular team-building activities and gatherings, providing great opportunities to unwind and engage with our vibrant team initiatives.
• A flexible hybrid working policy to choose where, how, and when you want to work.
• Opportunities to represent Faktion at industry conferences, technical events, and research-driven customer conversations.

At Faktion we build AI, and we use it thoughtfully in our own processes too. During your application, AI tools may help us with practical steps, like checking that your documents came through complete. They never decide who moves forward: every application is read by a real person on our team, and every decision is made by a human. Curious how this works, or how your data is handled? Just ask, we're happy to explain.