AI Solutions Developer

Hnm Solutions

Brussels, Belgium

3 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Languages

English

Job location

Brussels, Belgium

Tech stack

API

Agile Methodologies

Artificial Intelligence

Amazon Web Services (AWS)

Software Applications

JIRA

Azure

Cloud Computing

Computer Programming

Databases

Continuous Integration

Information Engineering

Elasticsearch

IntelliJ

Python

Machine Learning

Metadata

Meta-Data Management

MongoDB

Natural Language Processing

NLTK

NoSQL

TensorFlow

Software Engineering

SQL Databases

Unstructured Data

Management of Software Versions

Test Driven Development

PyTorch

Large Language Models

IT Architecture

Deep Learning

Generative AI

GIT

Pandas

Scikit Learn

Kubernetes

Cassandra

Data Management

Machine Learning Operations

Spacy

Data Pipelines

Docker

Job description

Develop and maintain AI-powered software applications integrating NLP, Machine Learning, and Generative AI.
Train custom machine learning and deep learning models (including transformer-based architectures) using structured and unstructured data.
Implement modern MLOps practices to support model training, deployment, monitoring, and versioning.

Architecture & Design

Design and implement IT architecture for AI solutions using the most appropriate AI components, techniques, and models.
Apply modern AI methodologies and best practices within enterprise IT environments.
Ensure proper integration with data pipelines, APIs, services, and enterprise systems.

Technical Collaboration & Communication

Present solution blueprints to both technical and business audiences.
Moderate discussions, gather feedback, and translate business needs into technical requirements.
Support best practices for data, master data, and metadata management.

Requirements

Do you have experience in TensorFlow?, Do you have a Master's degree?, * Programming: Excellent knowledge of Python and core AI/ML/NLP libraries (pandas, OpenAI, spaCy, PyTorch, TensorFlow, Transformers, scikit-learn, NLTK).

AI/ML Expertise: Proven experience with advanced AI techniques including LLMs, generative AI, RAG pipelines, agentic workflows (OpenAI, LangChain, CrewAI), function calling, MCP, and traditional ML algorithms.
Data Engineering: Strong experience with data pipelines, model and metadata versioning, and database integration (SQL and NoSQL such as Elasticsearch, MongoDB, Cassandra).
Cloud Platforms: Hands-on experience with AWS or Azure for deploying and scaling AI solutions.
Software Engineering: Practical knowledge of Git workflows, CI/CD, Docker, Kubernetes, and agile methodologies.
Architecture: Ability to design, document, test, and integrate AI/ML components within service-oriented enterprise architectures.

Desirable