Senior AI Engineer

DevologyX; Belgique; On-site; EUR 90,000 - 150,000; Full time

Job summary

DevologyX is seeking a hands-on Senior AI Engineer to design, build, and deploy production-ready AI solutions for enterprise financial services. You will develop Generative AI capabilities, integrate ML models, and implement MLOps practices.

You will work with Python, cloud platforms, and modern data tools to deliver scalable, secure AI applications, including fraud detection, risk analysis, and process automation, on a contract basis in Amsterdam with occasional on-site travel.

Qualifications

• Hands-on senior AI engineer with production-grade experience.
• Strong software engineering foundations and ability to translate business needs into scalable AI solutions.
• Experience deploying AI systems in regulated environments is a plus.
• Familiarity with MLOps and model monitoring is essential.

Responsibilities

• Design, develop, test, and deploy AI/machine learning solutions for enterprise apps.
• Build Generative AI apps using LLMs, RAG, and AI agents.
• Develop production-grade AI pipelines and model-serving infra.
• Implement AI-driven solutions for financial services use cases (fraud, risk, analytics).
• Develop APIs and integrate models into existing platforms.
• Work with large datasets to train, fine-tune, and evaluate models.
• Implement vector search, embeddings, and retrieval pipelines.
• Collaborate with architects, data engineers, and business stakeholders.
• Apply MLOps/LLMOps for deployment, monitoring, and versioning.

Skills

Problem solving

Independent worker

Fast-paced environment

Team collaboration

Tools

Python

Docker

Kubernetes

Cloud platforms

REST APIs

LangChain / LLM orchestration

Snowflake / Databricks

PyTorch / TensorFlow / Scikit-learn

Job description

Must be an EU Citizen / have authorization to work in the EU and travel on-site occasionally

Contract Duration: 6–12 months

Rate: Negotiable

Employment Type: Contract

Role Overview

We are seeking an experienced Senior AI Engineer to design, develop, and deploy advanced AI solutions within the financial services sector. The successful candidate will play a key role in building production-ready AI applications, developing Generative AI capabilities, and integrating machine learning models into enterprise systems.

This role suits a hands-on AI professional with strong software engineering skills, experience building and deploying AI models, and the ability to translate complex business requirements into scalable, secure, and reliable AI solutions.

Key Responsibilities

• Design, develop, test, and deploy AI and machine learning solutions for enterprise applications.
• Build and optimise Generative AI applications using large language models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents.
• Develop and maintain production-grade AI pipelines and model-serving infrastructure.
• Implement AI-driven solutions for financial services use cases, including fraud detection, risk analysis, customer intelligence, and process automation.
• Develop APIs and integrate AI models into existing applications and enterprise platforms.
• Work with large and complex datasets to train, fine-tune, evaluate, and optimise AI models.
• Implement vector search, embeddings, prompt engineering, and retrieval pipelines.
• Collaborate with AI architects, data engineers, software developers, and business stakeholders to deliver end-to-end AI solutions.
• Apply MLOps and LLMOps practices to automate model deployment, monitoring, versioning, and performance optimisation.
• Ensure AI applications meet requirements for scalability, reliability, security, and responsible AI.
• Troubleshoot production issues and continuously improve model performance, accuracy, latency, and cost efficiency.

Required Skills and Experience

• Proven experience as a Senior AI Engineer, Machine Learning Engineer, or in a similar technical role.
• Strong programming skills in Python, including experience developing production-grade applications.
• Practical experience with Generative AI, LLMs, RAG architectures, and AI agent development.
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Knowledge of LLM orchestration frameworks such as LangChain, LangGraph, or LlamaIndex.
• Experience working with vector databases, embeddings, semantic search, and information retrieval.
• Strong understanding of REST APIs, microservices, software engineering principles, and system integration.
• Experience deploying AI solutions on cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Familiarity with Docker, Kubernetes, CI/CD, and automated testing.
• Knowledge of MLOps and LLMOps, including model evaluation, monitoring, and deployment.
• Experience with data processing, SQL, data pipelines, and integrating structured and unstructured data.
• Strong problem-solving skills and the ability to work independently in a fast-paced environment.
• Experience within banking, financial services, fintech, or insurance.
• Knowledge of fraud detection, credit risk, financial forecasting, or customer analytics.
• Experience fine-tuning open-source or commercial LLMs for enterprise use cases.
• Familiarity with AI evaluation frameworks, guardrails, observability, and prompt optimisation.
• Experience with Snowflake, Databricks, or other modern data platforms.
• Understanding of GDPR, the EU AI Act, and responsible AI practices.
• Experience optimising AI applications for production performance, scalability, and cost.
• Location: Amsterdam, Netherlands
• Duration: Initial contract of 6–12 months
• Rate: Negotiable, depending on experience and technical expertise
• Start Date: To be confirmed
• Working Arrangement: To be confirmed

Ideal Candidate

The ideal candidate is a hands-on AI engineer who can take AI solutions from concept to production. They should have strong Python development skills, practical experience building LLM-powered applications, and a solid understanding of deploying and maintaining AI systems at scale.

Experience working with financial services data, enterprise AI applications, or regulated environments will be advantageous.