STAFIDE
Senior Agentic AI Engineer (ID: 4120)
Senior engineer who designs and deploys enterprise agentic AI: LLM applications, RAG with embeddings and vector databases, and multi-agent systems, built in Python on Azure/AWS with Databricks, Spark, Airflow, Docker/Kubernetes and MLOps. On-site in Amsterdam; 8–10 years of software/data/AI engineering experience expected.
- Compensation
- Not disclosed
- Location
- Amsterdam, Netherlands
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 8–10 years of overall experience in Software Engineering, Data Engineering, AI Engineering, or related technical fields
- Strong programming skills in Python and SQL
- Hands-on experience developing LLM-based applications and Agentic AI solutions
- Practical experience with RAG architectures, embeddings, vector databases, and multi-agent systems
- Experience with LangChain, LlamaIndex, Azure OpenAI, or equivalent AI frameworks and services
- Knowledge of Databricks, Apache Spark, and Apache Airflow for data processing and orchestration
- Experience with cloud platforms, particularly Microsoft Azure and/or AWS
- Familiarity with MLOps practices, model lifecycle management, and production AI deployments
- Experience with Docker, Kubernetes, and CI/CD pipelines
- Understanding of AI security, governance, monitoring, and enterprise-level deployment requirements
- Ability to work with cross-functional teams and translate business requirements into technical solutions
Full description
As a Senior Agentic AI Engineer, you will
- Design, develop, and deploy enterprise-grade AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent systems.
- Build intelligent AI applications, automation workflows, virtual assistants, and data-driven decision-support solutions.
- Develop and optimize AI workflows using frameworks such as LangChain, LlamaIndex, Azure OpenAI, or similar technologies.
- Implement embedding strategies and integrate vector databases to enable efficient semantic search and knowledge retrieval.
- Design, build, and maintain scalable AI and data pipelines on cloud platforms such as Microsoft Azure and AWS.
- Implement MLOps practices for model deployment, monitoring, evaluation, performance optimization, and retraining.
- Collaborate with business stakeholders, data engineers, software engineers, and other technical teams to deliver AI-driven solutions.
What You Bring to the Table
- 8–10 years of overall experience in Software Engineering, Data Engineering, AI Engineering, or related technical fields.
- Strong programming skills in Python and SQL.
- Hands-on experience developing LLM-based applications and Agentic AI solutions.
- Practical experience with RAG architectures, embeddings, vector databases, and multi-agent systems.
- Experience with LangChain, LlamaIndex, Azure OpenAI, or equivalent AI frameworks and services.
- Knowledge of Databricks, Apache Spark, and Apache Airflow for data processing and orchestration.
- Experience working with cloud platforms, particularly Microsoft Azure and/or AWS.
- Familiarity with MLOps practices, model lifecycle management, and production AI deployments.
- Experience with Docker, Kubernetes, and CI/CD pipelines.
- Understanding of AI security, governance, monitoring, and enterprise-level deployment requirements.
You should possess the ability to
- Design and implement scalable, reliable, and maintainable Agentic AI solutions.
- Develop and optimize LLM workflows to improve response quality, accuracy, and performance.
- Integrate enterprise data sources with AI applications using RAG, embeddings, and vector search.
- Build and manage AI and data pipelines across cloud environments.
- Evaluate AI models and workflows, identify performance issues, and implement improvements.
- Deploy, monitor, troubleshoot, and maintain AI applications in production environments.