Senior Machine Learning Engineer

Capgemini Engineering; Colombia; Presencial; COP 120.000.000 - 190.000.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Stable Employment

Learning & Development

Language Training

Health Coverage

Insurance Protection

Wellness Perks

Descripción de la vacante

Capgemini Engineering in Colombia is seeking a Senior MLOps Engineer with 6–10 years of experience in software engineering, MLOps, DevOps, cloud platforms, and distributed data processing to architect, deploy, and scale enterprise AI/ML solutions while guiding stakeholders.

You will work with data scientists and engineers to design reusable ML frameworks, optimize PySpark workloads, setup CI/CD pipelines, and deliver secure, production-ready ML platforms across cloud environments.

Formación

• Masters in computer science, data science, data engineering or a related field.
• 6-10 years of experience in Data Engineering or MLOps.
• Strong hands-on experience with PySpark optimization and cluster performance tuning.
• Experience with Azure Databricks, Apache Spark, Azure Machine Learning and Azure DevOps.
• Proficiency in Python, SQL and CI/CD tools.
• Experience with Agile Software Development.
• Proven experience in developing and deploying supervised ML models, pricing and billing recommendation systems.
• Experience building production-grade ML pipelines and enterprise AI platforms.
• Strong client-facing communication skills with experience gathering requirements and stakeholder management.

Responsabilidades

• Build associates closely with business stakeholders, data scientists, and engineering teams to translate requirements into scalable AI/ML solutions.
• Lead technical discussions, design workshops, and architectural reviews for end-to-end ML and MLOps implementations.
• Design, build, and maintain scalable, secure, production-ready ML platforms across cloud environments.
• Develop reusable frameworks, templates, and best practices to accelerate model development and deployment.
• Optimize and refactor large-scale PySpark applications for performance and cost efficiency.
• Configure, tune, and manage Spark clusters including executor sizing and resource allocation.
• Design and maintain CI/CD pipelines for automated model training, testing, deployment, and monitoring.
• Establish MLOps best practices including version control, experiment tracking, model registry, governance, and reproducibility.

Conocimientos

PySpark optimization

Python

SQL

CI/CD

Cloud platforms

Client-facing communication

Apache Spark

Educación

Masters in computer science/data science/data engineering

Herramientas

Azure Databricks

Apache Spark

Azure Machine Learning

Azure DevOps

CI/CD pipelines

Descripción del empleo

At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world's most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same

Your Role:

The ML/MLOps Engineering team is responsible for architecting, deploying, and scaling enterprise AI/ML solutions while providing technical leadership, architectural guidance, and engineering best practices across the machine learning lifecycle. The team focuses on building reusable ML frameworks and platform capabilities, optimizing and refactoring large-scale PySpark workloads, tuning Spark cluster configurations for performance and cost efficiency, and enabling scalable, production-ready ML systems. We are seeking a highly capable Senior MLOps Engineer with 6-10 years of experience in Software Engineering, MLOps, DevOps, Cloud Platforms, and Distributed Data Processing. The candidate will have proven experience collaborating directly with clients and stakeholders to gather requirements, define solution architectures, drive technical discussions, and deliver scalable, secure, and reliable machine learning platforms that accelerate business value and enterprise AI adoption.

• Build Associate closely working with business stakeholders, data scientists, and engineering teams to understand business requirements and translate them into scalable AI/ML and data engineering solutions.
• Lead technical discussions, solution design workshops, and architectural reviews to define end-to-end ML and MLOps implementation strategies.
• Design, build, and maintain scalable, secure, and production-ready ML platforms and infrastructure across cloud environments.
• Develop reusable frameworks, templates, and best practices to accelerate model development, deployment, and operationalization.
• Optimize and refactor large-scale PySpark applications to improve performance, scalability, reliability, and cost efficiency.
• Configure, tune, and manage Spark clusters, including executor sizing, resource allocation, partitioning strategies, caching, and workload optimization.
• Design, implement, and maintain CI/CD pipelines for automated model training, testing, deployment, and monitoring.
• Establish and enforce MLOps best practices, including version control, experiment tracking, model registry, governance, and reproducibility.

Your profile:

• Masters in computer science, data science, data engineering or a related field.
• 6-10 years of experience in Data Engineering or MLOps.
• Strong hands-on experience with PySpark optimization and cluster performance tuning.
• Experience with Azure Databricks, Apache Spark, Azure Machine Learning and Azure DevOps.
• Proficiency in Python, SQL and CI/CD tools.
• Experience with Agile Software Development.
• Proven experience in developing and deploying Supervised Machine Learning models, candidate will be working on Price Recommendation and Billing Recommendation systems.
• Experience building and managing production-grade ML pipelines and enterprise AI platforms.
• Strong client-facing communication skills with experience gathering requirements, managing stakeholder expectations, and delivering technical solutions.
• Ability to balance architecture, hands-on development, operational support, and strategic planning.

What We Offer

• Stable Employment: Permanent contract offering long‑term job security.
• Learning & Development: Access to a wide range of online training platforms and professional development resources.
• Language Training: Weekly virtual English classes and conversation sessions with certified instructors. Online Courses for different languages.
• Health Coverage: Comprehensive prepaid medical and dental plans.
• Insurance Protection: Life and accident insurance for peace of mind.
• Wellness Perks: Discounts and benefits through fitness and technology partnerships.

About Capgemini

At Capgemini Colombia, we aim to attract the best talent and are committed to creating a diverse and inclusive work environment, so there is no discrimination based on race, sex, sexual orientation, gender identity or expression, or any other characteristic of a person. All applications welcome and will be considered based on merit against the job and/or experience for the position.