Middle AI developer

  •  Wroclaw
  •  
  •  Engineering
Apply For Job

Description

The Middle AI Developer talent pool comprises skilled professionals with a strong foundation in artificial intelligence, machine learning, and data science who have advanced beyond junior-level responsibilities and are capable of independently contributing to AI-driven projects. Typically possessing 2–4 years of experience, these developers play a key role in the design, development, and deployment of AI models and intelligent systems across various domains.

Core Competencies:

  • Programming Proficiency: Strong command of Python (including libraries like TensorFlow, PyTorch, Scikit-learn, NumPy, and Pandas); experience with version control (Git), CI/CD, and containerization (Docker).

  • ML & AI Expertise: Solid understanding of supervised, unsupervised, and reinforcement learning; ability to select appropriate algorithms and evaluate model performance.

  • Data Handling: Experience in data preprocessing, feature engineering, and working with structured and unstructured datasets (e.g., tabular data, images, text).

  • Deployment & Integration: Familiarity with model serving frameworks (e.g., FastAPI, Flask, TorchServe) and deploying models to production environments, including cloud platforms (AWS, GCP, Azure).

  • Problem-Solving: Demonstrated ability to solve real-world business problems using AI techniques, including recommendation systems, NLP, computer vision, and time-series forecasting.

Typical Background & Education:

  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Applied Mathematics, or a related field.

  • Participation in AI-focused projects, hackathons, or internships.

  • Ongoing commitment to learning through certifications (e.g., DeepLearning.AI, Google ML Crash Course) and self-guided experimentation.

Soft Skills:

  • Strong analytical thinking and attention to detail.

  • Ability to work independently and collaborate effectively within cross-functional teams.

  • Clear communication of technical concepts to non-technical stakeholders.

Use Cases for Hiring from this Pool:

 

  • Scaling AI/ML teams for product development.

  • Enhancing business intelligence with predictive models.

  • Prototyping and productionizing new AI features.

  • Supporting senior engineers in research and experimentation.