MLOps Engineer

About Us

Coderio designs and delivers scalable digital solutions for global companies. We combine strong technical expertise with a product mindset to lead complex software initiatives end-to-end. We work with international clients, value autonomy and clear communication, and build long-term partnerships through technical excellence. 🌍 More information: http://coderio.com

This is a high-impact technical role. We are looking for a Senior Machine Learning Ops Engineer to join our team. You will own the architecture, development, and automation of end-to-end machine learning solutions. Operating within an established engineering team, you will be a key player in bridging the gap between data science and software engineering, evaluating cloud solutions, and deploying scalable machine learning models into production.

This is a hands-on execution role. You are responsible for the technical health of the ML lifecycle, the performance and cost-efficiency of the cloud architecture, and delivering high-quality, scalable solutions that drive business value.

What To Expect In This Role (Responsibilities)

• ML Architecture & Development: Architect and develop robust, end-to-end machine learning solutions.

• MLOps & Automation: Manage and automate the complete machine learning lifecycle, ensuring seamless transitions from development to production.

• Cross-Functional Collaboration: Collaborate closely with data engineers and data scientists to create highly scalable solutions and integrate them with other technical areas across the software development life cycle.

• Cloud & Architecture Strategy: Work on cloud solutions, actively evaluating the performance and cost-efficiency of potential architectures.

• Business Alignment: Interact with other teams and stakeholders to deeply understand business challenges and propose effective technical solutions.

• Mentorship & Advocacy: Communicate technical concepts clearly and teach teams how to properly use and adopt the new developments.

Requirements

• Educational Foundation: Bachelor’s degree in software engineering/computer science, or proven equivalent experience in data engineering or machine learning roles.

• ML Deployment Mastery: Proven hands-on experience implementing and deploying machine learning solutions in production environments.

• Core Stack Expertise: Advanced knowledge of Python, SQL, and Spark.

• Tooling & Infrastructure: Strong knowledge in (or similar to) Docker, Git, Bash Scripting, FastAPI, Sagemaker Studio, Airflow, and CloudFormation.

• Architecture & Integration: Solid understanding of data architectures and complex systems integration.

• Problem Solving: Demonstrated ability to troubleshoot and solve complex software system issues.

• Cloud Platforms: Extensive experience with modern cloud platforms, specifically AWS and GCP.

• Soft Skills: Must be a team player who effectively participates within the group. We are looking for a curious profile, a self-learner with a positive attitude, and excellent communication skills.

Nice to Have

• Domain experience in payments or other complex financial systems is a huge plus.