AI Engineer

Who we are

Conclusion Lifecycle is the Managed Services brand of Conclusion Iberia, focused on delivering long-term, scalable IT services across the full lifecycle, from build to run.

It brings together the Managed Services operation that was previously part of Neotalent Conclusion, with a clearer focus on lifecycle ownership, operational excellence, and long-term partnerships.

Conclusion Lifecycle also acts as the incubator for new lifecycle-based service offerings within Conclusion Iberia, supporting clients with sustainable, resilient, and continuously evolving digital solutions.

Find out more about us at www.conclusionlifecycle.com.

Role Overview

We are looking for a Senior AI Engineer - Azure, AI/MLOps & GenAI to integrate one of our Nearshore Teams working remotely for international clients accross The Netherlands, Germany and other European countries.

In this role, you will bridge the gap between Data Science and production-grade platforms, working across the full AI lifecycle. You will collaborate closely with data scientists, cloud engineers, and developers to build end-to-end AI, ML, and GenAI solutions that deliver measurable business impact for our clients.

What you will do

• Operationalise AI/ML models and GenAI applications using platforms such as Azure ML, MLflow, Databricks, and Kubeflow;
• Design, build, and maintain production-grade AI services and APIs in Python (e.g. FastAPI), including testing, versioning, and release strategies;
• Develop, deploy, and optimise ML models using libraries such as scikit-learn, TensorFlow, PyTorch, and Hugging Face;
• Design and implement GenAI solutions, including Retrieval Augmented Generation (RAG) and agent-based workflows, using frameworks like LangChain, Hugging Face, AI Foundry, and vector databases;
• Improve retrieval quality through chunking strategies, hybrid search, metadata filtering, reranking, and evaluation;
• Define and implement LLM evaluation frameworks and quality gates, including offline and online testing, regression testing, and RAG metrics;
• Implement monitoring, observability, and incident troubleshooting for LLM applications, including tracing, prompt management, and version control;
• Design and enforce guardrails and security controls for GenAI solutions, addressing risks such as prompt injection and data exfiltration.

What you are like

• Highly proficient in AI, ML, and software engineering, with hands-on experience taking models from experimentation to production;
• Experienced with Microsoft Azure services, including Azure ML, Azure DevOps, Functions, and managed identity patterns;
• Comfortable working with Databricks and Spark-based workloads, including orchestration of pipelines and clusters;
• Skilled in containerisation and orchestration using Docker and Kubernetes (AKS);
• Proficient in CI/CD practices and tooling such as Azure DevOps, GitHub Actions, or GitLab;
• Knowledgeable in Infrastructure as Code using tools like Terraform, Bicep, or Pulumi;
• Familiar with MLOps and LLMOps frameworks, including MLflow, Azure ML, and Kubeflow;
• Hands-on with the GenAI ecosystem, including RAG, prompt tuning, LLM evaluation, and vector databases;
• Clear and effective communicator, with English proficiency at B2 level or above.

Why join us

• Work on challenging projects for big clients
• Learn more about emerging technologies
• Possibility of working remotely
• Design your career plan
• Access to continuous training and certifications
• Evolve through our internal mobility programme
• … and much more!