Logo of Huzzle
  • Job
    Full-time
    Senior Level
  • Data
    Software Engineering
  • Madrid

AI generated summary

  • You should have a degree in a relevant field, 5+ years of MLOps experience, strong Python skills, knowledge of Azure and ML techniques, and excellent communication for cross-functional collaboration.
  • You will design and maintain ML pipelines, optimize workflows with teams, monitor model performance, ensure system reliability, implement CI/CD, and automate infrastructure using Azure services.

Requirements

  • University Degree in Data Science, Computer Science, Engineering or a related field
  • 5+ years of professional experience in deploying and maintaining machine learning systems in production, ideally within a technology-driven or data-intensive environment in multinational organizations
  • Passionate about leveraging MLOps and Generative AI technologies to ensure robust, scalable, and efficient ML system operations, with a focus on production-level implementation rather than solely experimentation
  • Proven experience collaborating with cross-functional teams, including data scientists, data engineers, and business stakeholders, to bridge the gap between research and production deployment
  • Strong expertise in reviewing and optimizing machine learning code to ensure production-readiness, scalability, and adherence to best practices
  • Excellent problem-solving skills with the ability to identify system bottlenecks, implement efficient solutions, and manage the lifecycle of ML models, including monitoring, retraining, and performance optimization
  • Exceptional communication skills to explain technical implementations, system architectures, and operational plans to diverse audiences, including non-technical stakeholders
  • Demonstrated ability to proactively identify operational challenges and design automation workflows, pipelines, and systems that align with business objectives
  • Strong interpersonal skills for effective collaboration across technical and operational teams
  • Proficiency in Python Programming
  • Microsoft Azure ecosystem and Distributed Processing (Databricks)
  • Knowledge of common supervised and unsupervised Machine Learning techniques
  • Knowledge of Deep Learning methods and Generative AI techniques would be a huge asset
  • Fluency in English

Responsibilities

  • Design, build, and maintain robust and scalable pipelines for training, testing, and deploying machine learning models, ensuring alignment with business needs and production-readiness
  • Collaborate with Data Scientists and Data Engineers to optimize workflows and streamline the transition from model development to deployment
  • Monitor and maintain the performance of machine learning models in production environments, implementing automation for retraining, versioning, and scaling as needed
  • Proactively address system bottlenecks and ensure models are highly available and secure
  • Partner with Data Platform and Operations teams to ensure seamless integration of ML solutions into the organization’s broader data ecosystem
  • Establish shared best practices for managing data and model lifecycle in compliance with governance policies
  • Lead initiatives to operationalize GenAI models and LLMOps workflows, focusing on their scalability and real-time applicability
  • Ensure that productionized GenAI solutions address practical business challenges and create measurable value
  • Leverage the Microsoft tech stack (e.g., Azure ML, Azure Databricks, and other Azure services) to build and maintain efficient and reliable MLOps solutions
  • Ensure alignment with corporate IT standards and guidelines while exploring innovative uses of Azure’s ML capabilities
  • Design and implement CI/CD pipelines tailored for ML models to ensure smooth and consistent delivery of production-grade solutions
  • Automate infrastructure provisioning, model monitoring, and logging to improve system reliability and performance

FAQs

What is the primary responsibility of the MLOps Engineer?

The primary responsibility of the MLOps Engineer is to ensure the efficient deployment and operationalization of machine learning models, enhancing AI capabilities while maintaining scalable and robust ML systems.

What type of team collaboration is expected in this role?

The MLOps Engineer is expected to collaborate with Data Scientists, Data Engineers, and other cross-functional teams to optimize workflows and streamline the transition from model development to deployment.

What technologies will the MLOps Engineer primarily work with?

The MLOps Engineer will leverage the Microsoft tech stack, including Azure ML, Azure Databricks, and other Azure services to build and maintain efficient and reliable MLOps solutions.

How many years of experience are required for this position?

A minimum of 5 years of professional experience in deploying and maintaining machine learning systems in production is required.

What programming language proficiency is necessary for this role?

Proficiency in Python programming is necessary for the MLOps Engineer role.

Is knowledge of Generative AI technologies important for this position?

Yes, experience with Generative AI technologies and LLMOps workflows is highly valued and emphasized for operationalizing AI solutions.

What are the educational qualifications required for the MLOps Engineer role?

A university degree in Data Science, Computer Science, Engineering, or a related field is required.

What should candidates expect during the recruitment process?

Candidates can expect to receive a reply within 2 weeks after the application deadline, regardless of the outcome.

Are there opportunities for automation within this role?

Yes, the role includes designing automation workflows and pipelines to improve system reliability, performance, and alignment with business objectives.

What are the key skills required for a successful MLOps Engineer?

Key skills include expertise in machine learning code optimization, problem-solving abilities, exceptional communication skills, and strong interpersonal skills for collaboration.

Manufacturing & Electronics
Industry
10,001+
Employees

Mission & Purpose

We are JTI - Japan Tobacco International - and we believe in freedom. We believe the possibilities are endless when you are free to choose. In fact, we have spent the last 20 years innovating, creating new and better products for our consumers to choose from. This is how we have grown to be present in 130 countries. But our business is not just business. Our business is our people. Their talents. Their potentials. We believe that when our people are free to be themselves, to grow, innovate and develop, amazing things happen for our business. At JTI, we believe that a diverse and inclusive environment is fundamental to generate innovation and achieve amazing results. And, because of this, all applications are considered without distinction of race, gender, disability, nationality, religion, sexual orientation, age or any other. We don't expect anyone to feel they have to change to fit in. At JTI, we respect you and your choices, and here you are free to think what you want. So if you don't want to fit in, JTI is for you. Sure, work is important, but at JTI family and our relationships come first. We support you, while you work, so you can support the people who matter. That's why our employees, from all over the world, choose to be part of JTI. It's why 9 out of 10 would recommend us to a friend. It's why we've been certified globally as a Top Employer for eight consecutive years. So when you're ready to choose a career you'll love, at a company you'll love, feel free to join us. #JoinTheIdea.