Senior Machine Learning Engineer

We are a consulting company with a bunch of tech-savvy and happy people!

We love technology, we love design, and we love quality. Our diversity makes us unique and creates an inclusive and welcoming workplace where every individual is highly valued.

With us, everyone can be themselves while respecting others for who they are. We believe that when an amazing mix of people come together and share their knowledge, experiences, and ideas, we can help our clients on a completely different level.

We are looking for someone who can start immediately and wants to grow with us!

With us, you have great opportunities to make real progress in your career and the chance to take on significant responsibility.

About the Role

We are seeking a highly skilled Machine Learning Engineer / MLOps Engineer to design, build, and deploy scalable machine learning systems. This role sits at the intersection of data science, software engineering, and DevOps, with a strong emphasis on productionizing models and maintaining robust ML pipelines.

Key Responsibilities

• Design, develop, and deploy machine learning models at scale

• Build and maintain end-to-end ML pipelines using modern MLOps practices

• Containerize applications and workflows using Docker

• Orchestrate ML workflows with Kubeflow or similar platforms

• Collaborate with data scientists to operationalize statistical and machine learning models

• Implement CI/CD pipelines for ML systems and data workflows

• Ensure reliability, scalability, and performance of ML infrastructure

• Apply advanced statistical modeling techniques to solve complex business problems

• Write clean, modular, and maintainable code using object-oriented programming principles

• Monitor, evaluate, and continuously improve deployed models

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field

• Strong experience in Machine Learning and Data Science

• Solid understanding of Statistical Modeling and Advanced Statistics

• Hands-on experience with Docker and containerized environments

• Experience with Kubeflow or other ML orchestration tools (e.g., Airflow, MLflow)

• Proficiency in at least one programming language (Python preferred)

• Strong knowledge of Object-Oriented Programming (OOP)

• Experience with CI/CD pipelines and DevOps practices

• Familiarity with cloud platforms (GCP, or Azure)

Preferred Qualifications

• Experience with large-scale distributed systems

• Knowledge of feature stores, model versioning, and monitoring tools

• Experience in deploying real-time or batch ML systems

• Familiarity with infrastructure-as-code (e.g., Terraform)

• Understanding of data engineering concepts and big data tools

Key Skills

• Machine Learning & Deep Learning

• MLOps & Model Lifecycle Management

• Docker & Containerization

• Kubeflow & Workflow Orchestration

• Statistical Analysis & Advanced Modeling

• Object-Oriented Programming

• CI/CD & DevOps Practices