Company Description
Inetum is a European leader in digital services. For businesses, public sector organizations, and society as a whole, the group’s 26,000 consultants and specialists work every day to create tangible digital impact: solutions that contribute to performance, innovation, and the common good.
With a presence in 19 countries, working closely with local communities, and alongside its major software developer partners, Inetum supports organizations in their digital transformation challenges with proximity, flexibility, and responsibility. Driven by its purpose, Inetum champions a vision of technology that is useful and well-managed, capable of unlocking the full potential of organizations and society: “Let’s make tech right.”
In 2025, the group generated revenue of 2.2 billion euros.
More information at: www.inetum.com
Job Description
Mission
Design, build and deploy enterprise-grade AI solutions, autonomous AI agents, and multi-agent orchestration frameworks that support digital transformation initiatives, with a focus on financial services, anti-financial crime processes, and scalable AI platforms. Contribute to the development of secure, reliable, and production-ready AI capabilities that enable organizations to leverage Generative AI and LLM technologies at scale.
Responsibilities:
- Architect and develop production-grade autonomous agents and multi-agent orchestration frameworks (e.g., LangChain, AutoGen, CrewAI).
- Design, build, and deploy AI solutions based on Large Language Models (LLMs) and agentic architectures.
- Guide technical implementations across multiple parallel squads, ensuring consistent and reusable architectural standards.
- Integrate LLMs with external APIs, proprietary tools, databases, and enterprise systems to enable advanced tool-calling capabilities.
- Optimize prompt engineering approaches, context management, state management, and long-running agent workflows.
- Implement monitoring, logging, evaluation, and observability frameworks for AI applications and autonomous agents.
- Apply MLOps/AIOps practices, including model versioning, testing, performance evaluation, and monitoring.
- Design and deploy AI solutions on cloud platforms, primarily AWS, while collaborating on Azure and Databricks environments where applicable.
- Collaborate with business, data science, engineering, and cloud teams to deliver AI-powered solutions in regulated environments.
Qualifications
Professional Experience
- 5-10 years of professional experience in AI Engineering, Software Engineering, Machine Learning, or related fields.
- Experience designing and implementing enterprise AI solutions.
- Experience building and deploying autonomous AI agents and multi-agent systems.
- Experience integrating Generative AI and LLM technologies into enterprise applications.
Technical Skills
- Strong Python development skills.
- Hands-on experience with AWS, including AWS Bedrock and AgentCore.
- Experience with AI/LLM integration patterns such as:
- MCP (Model Context Protocol)
- A2A (Agent-to-Agent communication)
- Structured Outputs
- Skills-based architectures
- Knowledge of agent orchestration frameworks such as LangChain, AutoGen, CrewAI, or similar.
- Experience with MLOps/AIOps practices:
- Versioning
- Testing
- Monitoring
- Evaluation frameworks
- Experience integrating AI solutions with APIs, databases, and enterprise platforms.
Nice to Have
- Experience with Azure AI services.
- Experience with Databricks.
- Experience in Financial Services, Insurance, AML, KYC, Fraud Prevention, or other regulated industries.
Additional Information
Benefits:
🌍 Full access to foreign language learning platform
💻 Personalized access to tech learning platforms
📈 Tailored workshops and trainings to sustain your growth
🩺 Medical Insurance
🍎 Meal tickets
🌄 Monthly budget to allocate on flexible benefit platform
🏋 Access to 7 Card services
🧘 Wellbeing activities and gatherings