AI Engineer Computer Vision Job in Milan – Deep Learning Role

📍 Location: eu

🏷 Type: Not specified

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Table of Contents

Job Overview

This role targets a Mid-Level AI Engineer specializing in Computer Vision and Deep Learning within a global manufacturing leader. You will design and deploy AI-driven production optimization systems using advanced neural architectures. Ideal candidates bring 2–5 years experience building end-to-end ML pipelines and applying CNNs, Transformers, and industrial vision systems. The position blends research and production engineering, making it highly relevant for engineers aiming to scale AI in real-world industrial environments.

🌍 Work Eligibility & Location

🌍 Visa Sponsorship: Not explicitly stated
✈️ Relocation Support: Likely available (global company)
🏠 Remote Type: Onsite
⏰ Timezone Requirement: CET (Italy-based)
🌐 Country Restrictions: Open to international candidates
🗣️ Language Requirement: English

This is an Onsite role based in Milan, requiring physical presence for integration with manufacturing systems. While visa sponsorship is not confirmed, global mobility pathways may exist due to the company’s international footprint. Candidates already in Europe or with work authorization will have an advantage.

💰 Salary Intelligence

💰 Official Salary: Not disclosed
📊 Estimated Range: €50,000 – €75,000/year
📈 Level: Mid-Level (2–5 years)

For Italy’s AI market, this compensation is competitive for a Mid-Level engineer, especially within a multinational industrial leader. Total rewards likely include bonuses, training programs, and international mobility, increasing the overall package value beyond base salary.

📊 Role Breakdown

This position blends applied AI research with production engineering. Approximately 40% of the role involves designing and training deep learning models using CNNs and Transformers for tasks such as object detection, segmentation, and anomaly detection. Another 30% focuses on data analysis and preprocessing, working with complex industrial datasets to improve model performance and reliability. Around 20% is dedicated to deployment and MLOps, including CI/CD pipelines, model monitoring, and performance optimization using tools like GitLab CI. The remaining 10% involves cross-functional collaboration and communicating AI results to stakeholders. The role requires strong ownership across the full lifecycle—from data ingestion to production deployment—making it highly practical and execution-focused rather than purely theoretical.

🧩 Required Skills & Fit

✅ Must: Strong experience with Deep Learning (CNNs, Transformers)
✅ Must: Hands-on Computer Vision (detection, segmentation, anomaly detection)
✅ Must: End-to-end MLOps and deployment pipelines
➕ Bonus: Experience with AWS or cloud ML infrastructure
➕ Bonus: Knowledge of Generative AI or multimodal models

📈 Difficulty & Competitiveness

⚡ Level: Moderate to High
📊 Experience barrier: 2–5 years
🧠 Skill complexity: Advanced applied AI + MLOps
🌍 Competition: High (global AI talent pool)

This role sits at a moderate to high difficulty level due to its expectation of full-stack AI delivery. Candidates must demonstrate real-world deployment experience, not just model training. With 2–5 years required, competition includes experienced engineers transitioning from tech, automotive, and industrial AI sectors.

🚀 Career Impact

⭐ Impact Rating: ⭐⭐⭐⭐☆
🏢 Brand value: Strong global industrial leader
📚 Skill growth: Production-grade AI systems
🚀 Future opportunities: Senior AI, ML Lead, Applied AI Architect

This role offers strong career acceleration for engineers aiming to specialize in industrial AI and scalable ML systems. Exposure to real manufacturing environments builds high-value applied AI expertise, positioning candidates for senior AI engineering roles, leadership tracks, or AI architecture positions in global organizations.

📋 Key Responsibilities

You will design and develop deep learning models using CNNs and Transformers for industrial computer vision applications. Responsibilities include analyzing complex datasets, building data pipelines, and optimizing model performance for real-world conditions. You will deploy AI solutions into production using MLOps frameworks, ensuring scalability and reliability. Collaboration is key—you will work cross-functionally with engineering and manufacturing teams to integrate AI into operational workflows. Additionally, you will communicate technical results to stakeholders and contribute to continuous system improvements.

🎯 Application Strategy

🎯 Best apply method: Direct via company careers portal
🔥 Highlight: End-to-end AI deployment experience
🔥 Highlight: Real-world Computer Vision projects
❌ Avoid: Overly academic or theoretical resumes
❌ Avoid: Lack of measurable project impact

🧠 Application Optimization (Adaptive)

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AI Engineer – Computer Vision role focused on deep learning, MLOps, and industrial AI deployment in manufacturing.

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🧠 Fit & Positioning Analysis

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📅 Application Signals

The job was recently posted, indicating high urgency from the employer. Early applicants will have a significant advantage before the pipeline fills. Given the global appeal of AI roles, expect strong competition, especially from candidates with production AI experience. Applying within the first 1–2 weeks is critical to maximize visibility.

🚀 Resume Optimization for This Role

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Tailoring your CV to this exact role significantly increases your chances.

Show CV Projects: Demonstrate object detection, segmentation, anomaly detection on industrial datasets

Highlight Deep Learning: Showcase CNN and Transformer architectures with training optimization results

Prove MLOps Experience: Include GitLab CI pipelines, model versioning, monitoring in production

Quantify Impact: Report accuracy gains, defect reduction, or efficiency improvements percentages

Demonstrate Deployment: Describe end-to-end pipelines from data preprocessing to scalable deployment

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✅ Job Source & Verification

This listing is sourced directly from the company’s official careers page, ensuring high source credibility and accuracy. All job details align with the original posting and have been structured for clarity. Last updated: April 2026, based on the official posting date and current availability.