About Alkmist
Alkmist is a B2B SaaS startup founded in 2024 in Ghent, on a mission to fix broken collaboration workflows in professional services. We replace email-driven chaos with structured, AI-powered Task Flows, started with audit and accountancy, expanding into banking, insurance, legal, M&A, and municipal governance.
We work with international network firms like RSM, Bakertilly, and Nexia, serving 5,000+ users across 64+ countries. We’re ISO 27001 certified, backed by €1.8M in funding.
Our backend is written in Rust. Our team is small, technical, and moves fast. If you’ve ever wanted to shape an AI/ML stack from the ground up inside a company that’s already generating revenue and signing enterprise clients, this is it.
The Opportunity
We’ve just secured a VLAIO R&D subsidy to build the next generation of our AI capabilities. This is a 21-month research & development project spanning six technical work packages, and we need an AI Engineer to drive the machine learning and NLP work at its core.
You won’t be fine-tuning models in isolation. You’ll be building production AI systems that process real financial documents, extract knowledge from meetings and emails, orchestrate multi-agent workflows, and run behavioral experiments, all within a platform that’s already live with paying enterprise customers.
Alongside the VLAIO project, you’ll contribute to our internal program to accelerate Alkmist’s growth trajectory and hit ambitious product-led growth targets.
What You’ll Work On
Document Classification & Intelligent Matching
Build ML models that automatically classify financial documents (depreciation tables, bank statements, trial balances, contracts) and match client uploads to the correct items in a Document Request List. Target: ≥95% classification accuracy, ≥70% contextual matching accuracy, processing folders in under 30 seconds.
AI-Curated Knowledge Base
Design NLP pipelines that extract actionable insights from meeting transcriptions, email threads, and uploaded documents, building a queryable, permission-aware knowledge base per client engagement. The goal: eliminate the repetitive questions caused by 30% annual staff turnover in audit.
Automated DRL Generation
Parse Belgian financial statements and trial balances using OCR + NLP, then auto-generate Document Request Lists with adaptive questioning logic. Target: ≥70% time savings vs. current Excel workflows, validated through crossover experimental design.
Pricing Prediction Engine
Develop ML models (gradient boosting and/or deep learning with categorical embeddings) to replace intuition-based pricing with data-driven predictions. SHAP-based explainability, confidence intervals, and drift monitoring included.
AI Agent Orchestration
Architect and build a multi-agent system where fine-tuned LLMs (Mistral, Qwen, DeepSeek) coordinate specialized agents for document validation, deadline tracking, reconciliation checks, and automated follow-ups. Sub-2-second orchestration decisions, 100+ concurrent users.
Behavioral Nudging & Experimentation
Design and run A/B experiments using multi-armed bandits to optimize client responsiveness, testing reminder timing, message framing, and escalation sequences. Statistically validated improvements in document submission turnaround times.
Your Profile
Required
- Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field (unfortunately, this is a hard requirement for this role)
- Strong foundation in machine learning: supervised/unsupervised learning, model evaluation, feature engineering, hyperparameter tuning
- Hands-on experience with NLP: transformer architectures, fine-tuning language models, embeddings, named entity recognition, text classification
- Proficiency in Python and the ML ecosystem (PyTorch, HuggingFace Transformers, scikit-learn, pandas)
- Experience deploying ML models in production: API serving, monitoring, retraining pipelines
- Familiarity with vector databases (Pinecone, Qdrant, pgvector) and retrieval-augmented generation patterns
- Self-starter mentality: you thrive in small teams where you own problems end-to-end
- Agentic native workflow
Nice to Have
- Experience with LLM orchestration frameworks (LangChain, LlamaIndex) or multi-agent systems
- Knowledge of OCR and document processing pipelines (PaddleOCR, Tesseract, document layout analysis)
- Familiarity with Rust (our backend language) or willingness to learn
- Experience with experimentation frameworks, A/B testing, or multi-armed bandit algorithms
- Background in financial services, audit, or accounting technology
- Dutch language proficiency (our financial document parsing targets Belgian/Dutch documents)
- MLOps experience: containerized model serving, CI/CD for ML, drift detection
What We Offer
- Greenfield AI/ML stack: you’re not inheriting legacy, you’re building the foundation
- Funded R&D runway: 21-month VLAIO-backed project with clear milestones and resources
- Real-world impact: your models ship to 5,000+ users across 64+ countries, not a research sandbox
- Small, senior team: direct collaboration with the founders and senior engineers, zero bureaucracy
- Competitive compensation: salary package commensurate with experience, with stock option plan
- Flexibility: hybrid work with a Ghent HQ (at the Korenlei) we care about output, not office hours
- Growth trajectory: join a company targeting €10M revenue by 2028
- Tech you’ll actually enjoy: Rust backend, cutting-edge LLMs (Mistral, DeepSeek, Qwen), vector search, behavioral science
How to Apply
Send your CV and a brief note on what excites you about this role to toto@alkmist.com. We don’t need a cover letter, a few sentences about a project you’re proud of tells us more.
Questions? Reach out to Toto De Brant (Co-founder) on LinkedIn or at the email above.