Machine Learning Engineer

· As a ML Engineer you will be part of a team that is responsible of the following operational activities:

• Design, Maintain and optimize data sourcing pipelines using ETL, CFT, Denodo (data virtualization), and Airflow to ingest, transform, and expose data for AI/ML use cases. Ensure seamless integration of new data sources (internal/external APIs, databases, or streaming platforms) while adhering to data governance and latency requirements.

• Maintain Python environments by proactively auditing dependencies, upgrading obsolete libraries, and enforcing version compatibility across development, testing, and production. Document and communicate changes to minimize disruption.

• Enforce Vulnerability management in production code by:

• Conducting regular security scans and patching critical vulnerabilities in pipelines, APIs, and dependencies.

• Implementing secure coding practices and collaborating with cybersecurity teams to mitigate risks.

• Automating compliance checks in CI/CD pipelines to block vulnerable code from deployment.

• Understand & support CI/CD workflows (Jenkins, GitLab CI/CD) for containerized ML models (Docker/Kubernetes), ensuring seamless deployment, versioning, and rollback capabilities.

• Troubleshoot and resolve complex incidents in QA/Production, ensuring minimal downtime and continuous improvement of AI services.

• Collaborate with Data Scientists and PROD IT teams to define production-ready architectures, balancing technical feasibility with business requirements (real-time responses, high-volume processing).

• Promote Software Engineering best practices— code quality, security, logging,… —within your squad.

• Stay ahead of AI/ML advancements (LLMs, Agentic AI) and propose innovative solutions to optimize workflows and reduce time-to-market.

Technical & Behavioral Competencies

• Mandatory : Expert

•  
  • >4 years of professional experience in Python Programming (OOP, decorators, code quality & security, performance optimization) 
  • Python environment building : strong uv skills, pip, mamba, micromamba,
  • ML engineering: MLOps, model versioning, deployment.
  • Containerization & orchestration: Docker, Kubernetes (scaling, resource management).
  • CI/CD pipelines: Jenkins, GitLab CI/CD (advanced workflows, artifact management).
  • Linux/Cloud infrastructure: Bash scripting, system administration, troubleshooting.
  • Database systems: PostgreSQL (query optimization, schema design).
  • Monitoring & incident management: Advanced logging & analysis, debugging complex issues.
  • Denodo Platform Proficiency – Ability to configure, query, and optimize virtual data layers using Denodo’s data virtualization tools, including creating logical views, data services, and API integrations.
    • Strong SQL skills to write efficient queries in Denodo’s VQL (Virtual Query Language) and optimize data retrieval for AI model training and validation.
    • Data Virtualization & Integration – Experience in connecting disparate data sources (e.g., databases, APIs, ETL pipelines) via Denodo to enable seamless data exploration for AI/ML workflows.
  • Airflow DAG Development & Orchestration – Ability to design, implement, and maintain scalable Directed Acyclic Graphs (DAGs) for AI/ML pipelines, including task dependencies, retries, and dynamic workflow generation
  • Airflow Integration & Optimization – Experience in:
    • Connecting Airflow to data platforms (Denodo, PostgreSQL, S3, etc.) and ML tools (e.g., MLflow, Kubeflow) via hooks, custom operators, or APIs.
      • Optimizing performance through parallelism tuning, executor selection (Celery/Kubernetes), and efficient XComs/artifact handling for large-scale workflows.