Machine Learning Engineer

📋 Job Overview

We are looking for an MLOps Engineer to take ownership of the full lifecycle of our Machine Learning models. In this strategic role, you will face a key technical challenge: managing and supporting our legacy processes (based on AWS SageMaker and Apache Flink) while driving the transition and adoption of our new tech ecosystem based on Databricks (Lakehouse, Feature Store, and Model Serving).

🏢 About Coderio

Coderio designs and develops scalable digital solutions for global companies. We combine deep technical expertise with a product-driven mindset to lead complex software initiatives end-to-end. Working with international clients, we value autonomy, clear communication, and long-term relationships built on technical excellence. 🌍 Learn more: coderio.com

🎯 The Role

We are looking for an MLOps Engineer to take ownership of the full lifecycle of our Machine Learning models. In this strategic role, you will face a key technical challenge: managing and supporting our legacy processes (based on AWS SageMaker and Apache Flink) while driving the transition and adoption of our new tech ecosystem based on Databricks (Lakehouse, Feature Store, and Model Serving).

✅ Key Responsibilities

• Feature Management & Deployment: Coordinate the creation, deployment, and rollout of new features and variables to strengthen core business models (Fraud, Payments, FX).
• Maintenance & Lifecycle: Design strategies for the preventive and corrective maintenance of production models, ensuring their retraining, stability, and performance.
• Continuous Monitoring: Oversee model observability to detect anomalies, performance degradation (data/concept drift), and real-time latency issues.
• Legacy Support: Ensure the operational continuity of current pipelines built on AWS SageMaker and streaming data flows with Apache Flink.
• Migration Strategy: Plan a secure, frictionless transition of legacy processes and infrastructure toward the company's newly defined technologies.

📌 Required Qualifications

• Experience: 4+ years of experience working with Machine Learning models in large-scale production, ideally in transactional or data streaming environments.
• SageMaker Stack: Hands-on experience managing and implementing flows in AWS SageMaker (pipelines, training, endpoints).
• Familiarity or experience with real-time data processing using Apache Flink (or similar streaming tools like Kafka / Spark Streaming).
• DevOps & Infrastructure: Solid experience with Infrastructure as Code (Terraform) and CI/CD automation tools.
• Language Proficiency: Fluent English (mandatory) for seamless daily communication with global, multicultural teams.

⭐ Desirable Experience

🎁 Benefits

🛂 Visa & Eligibility