Machine Learning Engineer
Wire IT, a Portuguese IT consulting company, is hiring a Machine Learning Engineer (MLOps) for a hybrid role (2 days/week in office) in Vila Nova de Gaia/Porto. The job centers on productionizing ML: Python services, CI/CD with MLflow, model deployment, monitoring, retraining and rollback, batch and real-time serving, Spark, and Azure with Infrastructure as Code.
- Compensation
- Not disclosed
- Location
- Porto, Portugal
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 3+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Minimum 3 years of professional experience in Software Engineering or Machine Learning Engineering
- Strong Python skills, including testing, packaging, API development, and code review
- Practical experience deploying and operating ML models in production environments
- Experience with CI/CD pipelines, model registration, and lifecycle management using MLflow or similar platforms
- Experience building reproducible ML training workflows and managing model deployment, monitoring, retraining, and rollback
- Experience with batch and real-time model serving
- Knowledge of containers and distributed processing using Spark
- Experience implementing model monitoring, data and prediction drift detection, alerting, and SLOs
- Working knowledge of Microsoft Azure and Infrastructure as Code concepts
- Understanding of the end-to-end Machine Learning lifecycle
- Experience developing APIs and integrating ML models into business applications and operational systems
- Knowledge of model evaluation metrics, production monitoring, and performance optimization
- Ability to refactor data science code into maintainable, tested production services and reusable libraries
- Strong understanding of MLOps practices, production readiness, incident response, and model lifecycle management
- Bachelor's degree in Computer Science, Software Engineering, or related field, or equivalent practical experience
- Fluent English (mandatory)
Nice to have
- Experience with real-time decisioning in payments, fintech, fraud detection, or credit-related environments
- Experience operating LLM-based applications in production (LLMOps, AgentOps, model evaluation, tracing, prompt versioning, AI guardrails)
- Experience with Feature Stores and continuous-training pipelines
- Knowledge of event-driven architectures using Kafka and low-latency inference solutions
- Experience with Azure ML, Terraform, and container-based serving platforms
- Understanding of model risk management and EU AI Act requirements
- Master's degree in Computer Science, Machine Learning, or a related field
- Azure, Databricks, or Machine Learning Engineering certifications
Full description
Based in Portugal, Wire IT is your specialized IT consulting partner with 18 years of experience driven by an experienced and senior team that helps clients make the right decisions in a fast-moving market.
Wire IT's ambition is to grow while keeping true to its nature: agile, people-centered, and fun. As we like to say: Its not only what we do, its how we manage that truly make the difference..
If you're looking for more than just a job - if you're looking for a place where you can truly make a difference - you might be the next person to join WIRE IT.
Role: Machine Learning Engineer (MLOps)
Location: Hybrid (2x/week in the office), Vila Nova de Gaia / Porto, Portugal
Required skills:
-
Minimum 3 years of professional experience in Software Engineering or Machine Learning Engineering.
-
Strong Python skills, including testing, packaging, API development, and code review.
-
Practical experience deploying and operating Machine Learning models in production environments.
-
Experience with CI/CD pipelines, model registration, and lifecycle management using MLflow or similar platforms.
-
Experience building reproducible ML training workflows and managing model deployment, monitoring, retraining, and rollback procedures.
-
Experience with batch and real-time model serving.
-
Knowledge of containers and distributed processing using Spark.
-
Experience implementing model monitoring, data and prediction drift detection, alerting, and Service Level Objectives (SLOs).
-
Working knowledge of Microsoft Azure and Infrastructure as Code concepts.
-
Understanding of the end-to-end Machine Learning lifecycle, from experimentation to production deployment.
-
Experience developing APIs and integrating ML models into business applications and operational systems.
-
Knowledge of model evaluation metrics, production monitoring, and performance optimization.
-
Ability to refactor data science code into maintainable, tested production services and reusable libraries.
-
Strong understanding of MLOps practices, production readiness, incident response, and model lifecycle management.
-