Develops applications using AI and ML models, often involved in integrating models into products. Connects AI models with common software using Python, APIs, and cloud-based AI services.
Trains, tests, and improves AI models for tasks such as object recognition or language processing. Creates data-learning algorithms, using tools like TensorFlow or scikit-learn.
Coordinates the development of AI products, defines objectives, monitors roadmaps, and bridges technical and business teams. Determines the direction for AI feature development, collaborates with developers and customers, and uses tools like Jira or Notion.
Automates processes related to training and deploying ML models, handles data management, versioning, and model monitoring. Takes care of ML model operations in production using MLflow, DVC, Docker, and cloud services.
Ensures the security of AI systems, protects them from attacks, and analyzes vulnerabilities in training data or models. Prevents AI model misuse, uses tools for auditing and testing AI system robustness.
Designs and develops user interfaces for interacting with AI applications. Delivers AI outputs to end users through web or mobile apps, using React, Vue, or Flutter.
Prepares, transforms, and manages data pipelines for AI model training. Builds data infrastructure for AI models using Apache Spark, Kafka, dbt, and cloud data warehouses.
Tests the quality of AI systems, automates tests, and verifies the accuracy of models and their integration. Check the AI functionality, automates tests using PyTest, Selenium, or custom scripts.
Specializes in developing, fine-tuning, and deploying large language models such as GPT, Azure OpenAI, BERT, or Copilot. Works with large-scale text models, using tools such as HuggingFace Transformers, LoRA, RAG, or PEF – must be familiar with databases (typically SQL).