Machine Learning Engineer
Turn models into reliable production services through automated pipelines, validation and monitoring, supported by AI-assisted engineering and human review.
Job Description
Mission Nexum is looking for a Machine Learning Engineer to turn models and experiments into reliable, observable and maintainable production systems. You will connect data, training, deployment and operations while preserving reproducibility and traceability. What you will do - Design and implement data, feature, training, validation and deployment pipelines - Package models as secure, tested and versioned services or batch workloads - Establish versioning and lineage for code, data, configurations and model artifacts - Automate model validation, CI/CD and controlled deployment across cloud and Kubernetes environments - Monitor prediction quality, latency, throughput, resource usage, failures and data or model drift - Investigate production issues and improve reliability, performance and operating cost - Collaborate with Data Scientists, AI Engineers, DevOps and platform teams on clear production acceptance criteria This job posting is addressed to both genders, in compliance with laws 903/77 and 125/91 on equal treatment in the workplace and against gender discrimination. We welcome candidates of all ages and nationalities, in accordance with legislative decrees 215/03 and 216/03. Nexum also encourages applications from people with disabilities, in compliance with current regulations.
How AI supports the role
Use approved coding and operations assistants to draft pipeline components, tests, configuration, documentation and diagnostic queries. AI-assisted log analysis may accelerate investigation, but conclusions must be checked against telemetry and system state. AI-generated changes require code review, automated tests and controlled release procedures.
Tool choices vary by project. AI-assisted work remains subject to human review, security controls and documented validation.
What You Bring
Production Python
Tested, maintainable software for data and model workloads.
MLOps Pipelines
Training, validation, versioning, deployment and reproducibility.
Cloud & Containers
Docker, Kubernetes, cloud services, APIs and CI/CD.
Model Operations
Observability, performance, failures, data drift and model drift.
How We Work
AI-Assisted Engineering
Approved AI tools accelerate analysis, implementation and documentation; people remain accountable for every released artifact.
Production Perspective
The work connects design, delivery, observability and continuous improvement rather than stopping at isolated prototypes.
Evidence & Traceability
Decisions are supported by tests, metrics, provenance and documented trade-offs.
Cross-Functional Delivery
Cloud, data, AI, security and business requirements are connected around real operating constraints.
