Machine Learning Engineer

Key Responsibilities:

Model Development:

Generative AI & Diffusion:

Transformer Architectures:

MLOps & Deployment:

ETL & Data Engineering:

Tooling & Annotation:

Performance Metrics:

R&D & Innovation:

Cross-Team Collaboration:

Job Requirements:

Communication Skills:

?Strong verbal and written communication skills.

?Ability to present AI concepts clearly to both technical and non-technical stakeholders.

Technical Proficiency:

?Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).

?Hands-on experience with computer vision (OpenCV, scikit-image, PIL).

?Experience with generative AI (GANs, diffusion models) and transformers for vision.

?Familiarity with MLOps tools (MLflow, Kubeflow, Airflow) and containerization (Docker, Kubernetes).

?Experience with cloud ML services (AWS Sagemaker, GCP Vertex AI, Azure ML).

?Strong ETL/data engineering skills for large-scale datasets, including synthetic data generation.

?Knowledge of image annotation workflows and tools (e.g., CVAT, Labelbox, Supervisely).

Preferred Background:

e-commerce, retail, or product image editing pipelines

?Proven track record of improving AI model performance with measurable metrics.

Analytical & Problem-Solving Skills:

?Strong ability to interpret results, troubleshoot issues, and iterate quickly.

?Creative problem-solving mindset for both research and production challenges.

Work Under Pressure:

?Comfortable delivering high-quality results under tight timelines.

?Positive, proactive, and adaptable to changing priorities.

Additional Info:

links to your portfolio, GitHub, or relevant AI projects

dedicated R&D time