Senior AI Machine Learning Engineer

hace 1 mes


Madrid, Madrid, España Welocalize A tiempo completo
About the Role

We are seeking a highly skilled Senior AI Machine Learning Engineer to join our team at Welocalize. As a trusted global transformation partner, we accelerate the global business journey by enabling brands and companies to reach, engage, and grow international audiences.

The successful candidate will be responsible for the discovery, design, development, and implementation leadership of machine learning solutions to improve our corporate services. This includes ownership or oversight of projects from conception to deployment with ML deployment techniques and technologies, specifically the appropriate AWS services, Docker, MLflow, and other tools.

Main Responsibilities
  1. Design and Develop Machine Learning Models: Design and develop machine learning models and algorithms for various aspects of the localization and business workflow processes, including business intelligence, machine translation, LLM finetuning, and quality assurance.
  2. Collaborate with Cross-Functional Teams: Collaborate with developers, data engineers, and business process stakeholders to gather requirements and identify areas for improvement.
  3. Project Ownership: Take ownership of key projects from conception to deployment, ensuring that they meet business requirements and maintain momentum and direction until delivery.
  4. Scientific Direction: Provide oversight of the discovery phases, data specification, and scientific direction of projects owned by ML engineers.
  5. Technique Selection: Evaluate and select appropriate machine-learning techniques and algorithms to solve specific problems.
  6. Performance Measurement: Develop tools and metrics to measure the performance of our models and systems.
  7. Deployment and Architecture: Deploy machine learning models into a large-scale AWS environment, including architecture design and deep knowledge of AWS best practices.
  8. Stakeholder Communication: Communicate effectively with all business stakeholders who may have little to no machine learning knowledge, explaining and exploring the benefits and limitations of various machine learning approaches in the context of their business goals.
  9. Model Deployment: Deploy machine learning models and algorithms using appropriate techniques and technologies, such as containerization using Docker and deployment to cloud infrastructure.
  10. Statistical Analysis: Perform statistical analysis and fine-tuning using test results.
  11. Implementation and Optimization: Implement and optimize machine learning models and technologies using Python, TensorFlow, Pandas, and other relevant tools and frameworks.
  12. Team Guidance: Provide guidance and support to members of the team, mentoring in best practices for machine learning management approaches, management environment, development, and deployment.
  13. Professional Development: Keep abreast of developments in the field, with a dedication to learning in the role.
Requirements

Education:
Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.

Experience:
5+ years of experience in developing and implementing machine learning models and algorithms.

Knowledge & Skills:
Strong knowledge of machine learning techniques and algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning.
Hands-on, high proficiency experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Experience with natural language processing (NLP) techniques and tools.
Ability to write robust, production-grade code in Python.
Significant experience with ML management technologies and deployment techniques. Must include enterprise experience with AWS and Docker. Examples of relevant (but not required) AWS services: Amazon Sagemaker, EC2, Lambdas, DynamoDB, RedShift.
Excellent problem-solving and analytical skills.
Strong communication and collaboration skills, with the ability to explain complex technical concepts to non-technical stakeholders.
Experience taking ownership of projects from conception to deployment, and mentoring more junior team members.



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