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