AI/ML Engineer

Typical experience levels for AI/ML Engineers

We see very varying experience levels and price levels in the market. It is wise to find the right experience level for your project. We typically advise clients to start with senior talents, and build out with more junior team members as the team gets established and well working.

Experience Levels

This is how TeamPilot defines the experience levels for AI/ML Engineers.

• Junior - 0-1 years of experience. Juniors are typically recently graduated. They focus on mastering Python, machine learning basics, and common libraries like Scikit-learn and TensorFlow
• Mid-Level - 1-3 years of experience. At mid-level, they have been exposed to several projects, and can deliver quality with their experience area. They take ownership of features, mentor juniors, and deepen knowledge of deep learning frameworks, model evaluation, and deployment pipelines.
• Senior - 4-8 years of experience. Seniors have a broad experience from several projects and technologies. They lead technical decisions, architect solutions, and drive best practices.
• Lead - 8+ years of experience. A Lead has typically worked for different companies, and has basic experience of working in other roles. They manage teams, define technical strategy, and bridge technical and business requirements.

Typical Rate Levels

The contractor rates vary a lot based on the country and local price levels. Contractors in hot spots like London have higher prices, while people from smaller cities in lower cost countries may have lower rates for the same skill sets. Please note that TeamPilot always ensures the candidate to have the correct experience level, independent on location. Specific in-demand skills can also increase the rate level.

• Junior: €45 - €60
• Mid-Level: €60 - €75
• Senior: €75 - €95
• Lead: €95 - €120+

How TeamPilot evaluates AI/ML Engineers

When evaluating candidates as AI/ML Engineers, we for example:

• Assess their understanding of ML algorithms and statistics
• Review projects involving model training and deployment
• Check for experience with ML frameworks like TensorFlow or PyTorch
• Evaluate coding skills and data pipeline understanding
• Look for real-world applications and impact of ML solutions