Mid Machine Learning Engineer

Meet Genesis

Genesis Digital Solutions is a consulting and technology company that helps organizations turn ideas into measurable business impact through digital expertise and AI innovation. Founded in 2018, we operate across Portugal and the United Arab Emirates, supporting clients in multiple industries with intelligent, scalable, and secure solutions. We combine strategy, engineering, and emerging technologies such as AI, Data, Web3, and Digital Product Development to drive transformation end to end. Our focus is on building practical, human-centered solutions and long-term partnerships that enable sustainable growth, innovation, and real-world results.

Your Next Challenge

We are looking for a Mid Machine Learning Engineer to help bring machine learning models into production and ensure they are reliable, scalable, and properly monitored. You will work closely with Data Scientists and Data Engineers to productionize ML solutions, build and maintain deployment pipelines, monitor models in production, and optimize their performance.

What You’ll Work On

• Productionize machine learning models developed by Data Scientists and deploy them reliably into production environments
  
  


• Build and maintain ML-focused CI/CD and deployment pipelines
  
  


• Manage machine learning models throughout their production lifecycle
  
  


• Implement monitoring and basic alerting for model performance, failures, and operational metrics
  
  


• Optimize model response times and overall production performance
  
  


• Contribute to retraining automation initiatives and continuous improvement of ML workflows
  
  


• Develop clean, structured, reusable, and testable Python code
  
  


• Collaborate closely with Data Scientists and Data Engineers on model deployment and data transformation initiatives
  
  


• Deploy models using Docker and/or Kubernetes
  
  


• Develop and maintain model-serving APIs using FastAPI or Flask
  
  


• Apply pragmatic solutions to production challenges and take ownership of operational deliverables
  
  


• Contribute to the development of MLOps practices, processes, and automation from the ground up.
  
  

What We’re Looking For

• More than 3 years of professional experience in Machine Learning
  
  


• Strong Python skills, including project structuring, reusable and testable code, object-oriented programming, and design patterns
  
  


• Experience with Python testing frameworks such as pytest or unittest
  
  


• Strong SQL skills for querying and manipulating data
  
  


• Experience building and maintaining CI/CD pipelines using tools such as GitHub Actions or Jenkins
  
  


• Understanding of MLOps fundamentals, including Git versioning, pull requests, code reviews, and ML-specific versioning practices
  
  


• Experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn
  
  


• Experience deploying machine learning models using Docker and/or Kubernetes
  
  


• Understanding of model optimization and compression
  
  


• Experience or familiarity with cloud environments such as AWS, GCP, or Azure for ML workloads
  
  


• Experience building model-serving APIs using FastAPI or Flask
  
  


• Strong collaboration and communication skills, particularly when working with Data Scientists and Data Engineers
  
  


• Ability to take ownership of deployments, pipelines, production fixes, and operational deliverables
  
  


• Strong problem-solving skills and ability to work independently with appropriate guidance.
  
  

Bonus Points

• Basic knowledge of Java or Scala
  
  


• Experience establishing MLOps practices in environments where infrastructure and processes are still being developed
  
  


• Experience with automated model retraining
  
  


• Experience with model monitoring and observability
  
  


• AWS, GCP, or Azure certifications
  
  


• Experience working in environments with highly scalable ML workloads.
  
  

Why You’ll Stay

• A workplace that values innovation and personal growth
  
  


• Opportunities to work on high-impact projects for leading clients
  
  


• Flexible hours and hybrid work options
  
  


• Support for professional development, including training and certifications
  
  


• Health and life insurance
  
  


• 25 days of annual leave