Data Engineer and Machine Learning Engineer

Description

Job Summary: Serve as a technical reference in data architecture and AI, designing, developing, and evolving the infrastructure that supports data- and AI-driven solutions. Key Highlights: 1. Technical reference in data architecture and artificial intelligence 2. Responsible for developing and evolving the data and AI infrastructure 3. Technical mentor for the team and promoter of engineering innovation Role Objective Serve as the technical reference for UniScale's data architecture and artificial intelligence, responsible for designing, developing, and evolving the infrastructure that supports the organization's data- and AI-driven solutions. Will be responsible for building data pipelines, developing proprietary Machine Learning models, implementing MLOps practices, ensuring data governance and quality, and delivering scalable services that support intelligent products. Will also serve as the team’s technical mentor, promoting the evolution of engineering practices, innovation, and AI-driven development. Key Data Engineering Responsibilities * Design, implement, and evolve scalable, highly available data architectures. * Model and administer relational, non-relational, vector, and graph databases. * Develop, maintain, and optimize data ingestion, transformation, and delivery pipelines (ETL/ELT). * Ensure data quality, integrity, security, and governance across organizational solutions. * Implement data versioning, traceability, and access control strategies. Machine Learning Development * Develop, train, validate, and deploy proprietary Machine Learning models. * Evaluate model performance, quality, and accuracy, driving continuous improvement. * Build APIs and services to expose developed models. * Monitor models in production, identifying performance degradation, retraining needs, and optimization opportunities. * Support adoption of modern Artificial Intelligence and Machine Learning techniques applied to business use cases. MLOps and Solution Architecture * Design and evolve MLOps pipelines for model training, validation, publishing, and monitoring. * Implement continuous integration and continuous delivery (CI/CD) processes for AI solutions. * Develop solutions based on microservices and distributed architectures. * Administer cloud environments and infrastructure required to run AI applications. * Work with containerization using Docker and orchestration via Kubernetes. * Implement solutions using messaging systems and asynchronous processing. Integration and Intelligent Solutions * Develop integrations between systems, services, and Artificial Intelligence platforms. * Orchestrate workflows and automations using tools such as N8N, Flowise, or equivalent technologies. * Leverage modern AI-assisted development tools to accelerate development and improve delivery quality. * Collaborate directly with the AI Solutions Engineer, providing data structures and models that enhance the organization’s intelligent products. Technical Leadership and Continuous Improvement * Serve as the technical reference in Data Engineering and Machine Learning. * Mentor and technically develop team members. * Support architectural decisions related to data and AI solutions. * Propose continuous improvements to processes, technologies, and development standards. * Contribute to fostering an AI-First culture and adopting engineering best practices. Requirements Education * Bachelor’s degree completed in Computer Science, Computer Engineering, Software Engineering, Information Systems, Data Science, or related fields. Technical Knowledge * Solid experience in Data Engineering. * Advanced proficiency in Python. * Experience in modeling and administering SQL and NoSQL databases. * Knowledge of vector and graph databases. * Experience with Machine Learning frameworks such as Scikit\-learn, TensorFlow, or PyTorch. * Experience in developing, training, deploying, and monitoring Machine Learning models. * Development of REST APIs for exposing models and services. * Knowledge of MLOps practices, including versioning, CI/CD for AI, and model monitoring. * Experience with AI workflow orchestration tools such as N8N, Flowise, or similar. * Hands-on experience with modern AI-assisted development tools. * Experience in cloud environments (AWS, Azure, GCP, or similar). * Knowledge of Docker and Kubernetes. * Experience with microservice-based architecture. * Knowledge of messaging systems such as Kafka, RabbitMQ, Redis Streams, or equivalents.