AI Machine Learning Engineer

Role and mission

The AI Machine Learning Engineer turns the prototypes built by data scientists into reliable, scalable, maintainable systems. The focus is MLOps: reproducible pipelines, deployment under SLAs, drift monitoring, retraining and cost control. The role complements the AI Data Scientist upstream and the AI Data Engineer on infrastructure. A London fintech serving real-time scores and a Gulf telco operating recommendation systems both depend on it.

Main responsibilities

• Implement reproducible, versioned training pipelines.
• Deploy models meeting availability SLAs and latency requirements.
• Build monitoring for drift detection and quality assurance.
• Manage periodic retraining and version rollouts.
• Optimise infrastructure cost and energy consumption.
• Ensure compliance with AI Act obligations for high-risk systems.

Technical skills

• Advanced Python and clean software engineering practice
• ML frameworks: PyTorch, TensorFlow, scikit-learn
• MLOps tooling: MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML
• Docker, Kubernetes and CI/CD for ML
• APIs and microservices (FastAPI, gRPC)
• Monitoring (Prometheus, Grafana, Evidently, Arize) and serving (BentoML, Triton, vLLM)

Cross-functional skills

• Systems thinking and failure anticipation
• Communication across heterogeneous teams
• Responsibility for production robustness
• Data versioning and governance discipline
• Fairness and bias monitoring across segments

Training pathway and certification

The usual path is a degree in computer science or engineering, specialised ML coursework and substantial software engineering experience. Many ML Engineers transition from software development with a solid mathematical foundation. Vendor certifications — AWS ML Specialty, Google Cloud Professional ML Engineer, Azure AI Engineer, Kubernetes CKA/CKAD — are common complements.

Market context

Demand is strong in finance, retail, manufacturing, healthcare, telecommunications and central public administration. In Italy juniors earn €35,000–€55,000, mid-level €55,000–€80,000 and seniors €80,000–€120,000, with advanced MLOps and GenAI specialists at €130,000–€160,000; senior international roles exceed €200,000. A New York retailer and a Riyadh bank recruit against the same competency set. Related UNI 11621-8 roles: AI Data Scientist and AI Deep Learning Engineer. Return to the profiles overview.

European Digital Credential by AIPIA

AIPIA is authorised by the European Commission as an issuer of European Digital Credentials (EDC) carrying the eIDAS electronic seal. The credential is cryptographically verifiable, stored in the European digital wallet and recognised across all 27 member states. Issuance follows a defined route: AIPIA membership, submission of a competency dossier (CV, training, experience and project portfolio), assessment by the technical committee against the UNI 11621-8 criteria, an optional interview, and issuance with a QR verification code. The credential is valid for three years and renewable through continuing professional development. Two further routes exist: third-party certification under ISO/IEC 17024 — for which no Italian body is yet accredited, the process being in progress — and a professional quality attestation under Article 7 of Italian Law 4/2013 for qualifying members.