The Role
Design, build, deploy, and optimize enterprise Agentic AI and LLM applications using RAG, multi-agent systems, embeddings, and vector databases. Develop scalable AI and data pipelines across Azure and AWS, applying MLOps, Docker, Kubernetes, and CI/CD practices. Ensure solutions are secure, governed, reliable, and production-ready while collaborating with business, data, and engineering teams to deliver practical AI-driven solutions.
Summary Generated by Built In
As a Senior Agentic AI Engineer, you will:
- Design, build, and deploy enterprise-grade Agentic AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent systems.
- Develop AI-powered automation, intelligent assistants, and data-driven decision platforms.
- Build Agentic AI applications using LLMs, RAG, embeddings, and vector databases.
- Design and optimize AI workflows using LangChain, LlamaIndex, and cloud AI services.
- Design and implement scalable AI and data pipelines across Azure and AWS environments.
- Implement MLOps practices covering model deployment, monitoring, evaluation, and retraining.
- Ensure AI solutions are scalable, secure, reliable, governed, and production-ready.
- Collaborate with business and engineering teams to translate requirements into AI-driven solutions.
- Contribute to the continuous improvement of AI platforms, workflows, and engineering practices.
What You Bring to the Table:
- 8–10 years of overall professional experience in Software Engineering, Data Engineering, or AI Engineering.
- Strong hands-on programming experience with Python and SQL.
- Proven hands-on experience with LLMs, Agentic AI, RAG, embeddings, and vector databases.
- Experience with LangChain and/or LlamaIndex.
- Experience with Azure OpenAI or similar AI frameworks and cloud AI services.
- Knowledge of Databricks and Apache Spark.
- Experience with Airflow for workflow and pipeline orchestration.
- Strong understanding of cloud platforms such as Microsoft Azure and/or AWS.
- Experience with Docker and Kubernetes.
- Experience with CI/CD practices and modern software delivery pipelines.
- Understanding of MLOps, including model deployment, monitoring, evaluation, and retraining.
- Understanding of AI platform security, governance, scalability, and reliability.
You should possess the ability to:
- Design and develop scalable, production-ready Agentic AI and LLM-based applications.
- Build effective RAG pipelines using embeddings and vector databases.
- Design AI workflows and multi-agent solutions using modern AI frameworks.
- Develop reliable AI and data pipelines using cloud platforms and data engineering technologies.
- Work effectively with Python and SQL to build AI and data-driven solutions.
- Deploy, monitor, evaluate, and continuously improve AI/ML models and applications.
- Apply MLOps practices throughout the AI solution lifecycle.
- Work with containerized workloads using Docker and Kubernetes.
- Implement CI/CD practices for reliable and repeatable AI application delivery.
- Identify and address security, governance, scalability, and reliability considerations in enterprise AI platforms.
- Collaborate effectively with business stakeholders, data teams, software engineers, and other technical teams.
- Translate complex business requirements into practical AI-driven solutions.
- Work independently and take ownership of technical deliverables in a production-focused environment.
What we bring to the table:
- The opportunity to work on enterprise Agentic AI and Generative AI initiatives.
- Exposure to cutting-edge technologies including LLMs, RAG, multi-agent systems, embeddings, and vector databases.
- Opportunities to work with LangChain, LlamaIndex, Azure OpenAI, Databricks, Spark, and Airflow.
- Experience across Azure and AWS cloud environments.
- Exposure to modern MLOps, Docker, Kubernetes, and CI/CD practices.
- Opportunities to build scalable, secure, and production-ready AI platforms and solutions.
- Collaboration with business and engineering teams on AI-powered automation and intelligent decision platforms.
Let’s Connect
Want to discuss this opportunity in more detail? Feel free to reach out.
Recruiter: Asha Krishnan
Phone: +31 20 369 0609 ; Extn :146
LinkedIn: https://www.linkedin.com/in/asha-krishnan
Skills Required
- 8-10 years of professional experience in Software Engineering, Data Engineering, or AI Engineering
- Hands-on programming experience with Python and SQL
- Hands-on experience with LLMs, Agentic AI, RAG, embeddings, and vector databases
- Experience with LangChain and/or LlamaIndex
- Experience with Azure OpenAI or similar AI frameworks and cloud AI services
- Knowledge of Databricks and Apache Spark
- Experience with Airflow for workflow and pipeline orchestration
- Strong understanding of Microsoft Azure and/or AWS
- Experience with Docker and Kubernetes
- Experience with CI/CD practices and modern software delivery pipelines
- Understanding of MLOps, including model deployment, monitoring, evaluation, and retraining
- Understanding of AI platform security, governance, scalability, and reliability
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The Company
What We Do
STAFIDE is a Netherlands-based niche technology talent consulting company operating across Europe. It helps organizations identify, recruit, and deploy technology professionals in areas including cybersecurity, cloud engineering, software development, data analytics, ERP, infrastructure, and digital transformation. Its services include recruitment, workforce engagement, secondment, onboarding support, workforce deployment, and workforce analytics that support technology hiring and expansion.
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