Help design, build and continuously improve the clients online platform.
Research, suggest and implement new technology solutions following best practices/standards.
Take responsibility for the resiliency and availability of different products.
Be a productive member of the team.

Requirements

8+ years of Software, Data, or AI Engineering experience.
Senior Agentic AI Engineer responsible for designing, building, and deploying enterprise AI solutions using LLMs, RAG, and multi-agent systems.
Develop AI-powered automation, intelligent assistants, and data-driven decision platforms, with a focus on scalability, security, and production readiness.
Develop Agentic AI applications using LLMs, RAG, embeddings, and vector databases.
Build and optimize AI workflows using LangChain, LlamaIndex, and cloud AI services.
Design scalable AI and data pipelines on Azure/AWS.
Implement MLOps practices including model deployment, monitoring, evaluation, and retraining.
Collaborate with business and engineering teams to deliver AI-driven solutions.
Ensure security, governance, and reliability of AI platforms.
Strong Python and SQL programming skills.
Hands-on experience with LLMs, Agentic AI, RAG, embeddings, and vector databases.
Experience with LangChain, LlamaIndex, Azure OpenAI, or similar AI frameworks.
Knowledge of Databricks, Spark, Airflow, and cloud platforms (Azure/AWS).
Experience with Docker, Kubernetes, and CI/CD.

Benefits

Fixed Term Employment Contract
Work-Life Balance
Flexible Work Mode

What GeekSoft Consulting Offers

International, collaborative consulting culture with clear career development paths.
Comprehensive Dutch benefits package emphasizing well-being, mobility, and career growth.

Vereiste vaardigheden

Agentic AI and multi-agent system designLarge Language Model (LLM) engineeringRetrieval-Augmented Generation (RAG) implementationEmbeddings and vector database integrationLangChain and LlamaIndex framework usageCloud AI services (e.g., Azure OpenAI, AWS AI)Designing scalable AI and data pipelines on Azure/AWSMLOps: model deployment, monitoring, evaluation, retrainingDatabricks, Spark, and AirflowContainerization, orchestration, and CI/CD (Docker, Kubernetes)Python programmingSQL proficiencyAI platform security, governance, and reliabilitySystem resiliency and availability engineeringCross-functional collaboration with business and engineering teams

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