Senior Machine Learning Engineer

Capgemini Engineering; Bogotá; Presencial; COP 120.000.000 - 180.000.000; Jornada completa

Descripción de la vacante

Capgemini Engineering in Bogotá, Colombia seeks a Senior MLOps Engineer to architect, deploy, and scale enterprise AI/ML solutions. You will collaborate with data scientists and engineers to deliver secure, production-ready ML platforms across cloud environments.

We require 6–10 years in Data Engineering or MLOps, strong PySpark optimization, and experience with Azure Databricks, Spark, and CI/CD. Join a global leader delivering scalable AI capabilities for diverse industries.

Formación

• Master’s 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.

Responsabilidades

• Collaborate with stakeholders to translate requirements into scalable ML solutions.
• Lead architecture reviews, solution design workshops, and end-to-end ML/ MLOps strategies.
• Design, build, and maintain scalable, secure, production-ready ML platforms across cloud environments.
• Develop reusable frameworks, templates, and best practices to accelerate model development, deployment, and operation.
• Optimize and refactor large-scale PySpark applications for performance, scalability, reliability, and cost efficiency.
• Configure, tune, and manage Spark clusters, including executor sizing and resource allocation.
• 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.

Educación

Master’s in computer science, data science, data engineering or related field

Herramientas

PySpark optimization

Azure Databricks

Apache Spark

Azure Machine Learning

Azure DevOps

Python

SQL

CI/CD tools

Agile development

Stakeholder management

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 mostinnovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as theyprovide 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 arethe same.

Job Description

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:

• Master’s 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.

Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.