Senior AI Engineer / AI Architect

Required Skills & Experience

• Master's degree in Computer Engineering, Computer Science, Data Science, or related STEM fields.
• 5+ years of experience in AI/ML software development in Python, Scala, or similar.
• Strong experience in solution design and software architecture.
• Solid knowledge of Machine Learning, Deep Learning, and Generative AI.
• Experience with MLOps frameworks (MLflow, DVC, Weights & Biases, etc.).
• Strong SQL skills for data modeling, querying, and ETL processes.
• Hands-on experience with Google Cloud Platform and Microsoft Azure.
• Experience with CI/CD, code repositories, and infrastructure-as-code concepts.
• Proven experience leading technical teams and contributing to pre-sales and delivery.

• Experience with advanced model training and fine-tuning techniques (PEFT, LoRA, superconvergence).
• Knowledge of semi-supervised and self-supervised learning.
• Experience designing multi-cloud AI platforms.
• Familiarity with AGILE frameworks (Scrum, Kanban).
• Experience in regulated or large enterprise environments.

• Fluent professional proficiency in Italian and English.

Role Overview

The Senior AI Engineer / AI Architect is a hybrid role combining hands-on AI engineering with end-to-end AI architecture design.
The role is responsible for designing, building, and industrializing scalable AI solutions, while also defining architectural standards, cloud strategies, and governance models aligned with enterprise IT ecosystems.

This profile acts as a technical reference point for AI initiatives, bridging business needs, data platforms, cloud infrastructure, and production-grade AI systems.

Key Responsibilities

AI Engineering (Hands-on)

• Design, develop, train, fine-tune, and deploy Machine Learning and Generative AI models.
• Build and operate production-ready AI systems (batch, real-time, APIs).
• Implement robust MLOps pipelines (CI/CD, model versioning, monitoring, retraining).
• Optimize performance, scalability, reliability, and cost of AI workloads.
• Integrate AI solutions into enterprise applications and data platforms.

AI & Data Architecture

• Design end-to-end AI architectures: data ingestion → feature engineering → model training → serving → monitoring.
• Define reference architectures, patterns, and best practices for AI and MLOps.
• Select and evaluate technologies, frameworks, and cloud-native services.
• Design architectures compliant with security, governance, and regulatory requirements.
• Support pre-sales activities with solution design, estimates, and technical proposals.

Cloud Architecture (Google Cloud & Azure)

• Design and govern AI solutions on Google Cloud Platform (GCP) and Microsoft Azure.
• Deep understanding of:
  • GCP: Vertex AI, BigQuery, Cloud Storage, Dataflow, GKE, IAM
  • Azure: Azure ML, Synapse, Data Factory, Azure Kubernetes Service, Blob Storage, Entra ID
• Define multi-cloud or hybrid-cloud AI architectures when required.
• Optimize cloud costs, scalability, availability, and security.
• Collaborate with Cloud Architects and IT teams to ensure enterprise alignment.

Leadership & Collaboration

• Act as technical lead and mentor for AI Engineers and Data Scientists.
• Collaborate with Cloud Data Architects, Data Engineers, Security, and Business stakeholders.
• Contribute to internal AI communities through research, technology scouting, and innovation initiatives.
• Support delivery, governance, and continuous improvement of AI platforms.