AI Engineer

We’re looking for an AI Engineer to join our team on a full-time or part-time (3–4 months) basis. In this role, you’ll design, train, optimize, and deploy neural network models that power our next-generation products. You’ll own the full model lifecycle — from data pipeline design to production monitoring — working closely with product managers, developers, and DevOps to bring research ideas to life.

About the Project: The project involves developing a solution to demonstrate the functional capability to consolidate and process data collected from a smart insole equipped with pressure and motion sensors. The solution should confirm that the acquired data can be structured, analyzed, visualized, and presented to the end user.

Key Responsibilities

• Research, prototype, and benchmark deep-learning architectures (CNNs, RNNs, Transformers, diffusion models, etc.).
• Build scalable data ingestion, preprocessing, and augmentation pipelines.
• Manage large-scale experiments and hyperparameter tuning using tools like Weights & Biases or MLflow.
• Train, fine-tune, and optimize models (supervised, unsupervised, generative, or multimodal).
• Develop and refine prompts for LLMs (GPT, Claude, Hugging Face models).
• Deploy models to production environments (Docker, Kubernetes, cloud platforms).

• Monitor and maintain model performance, detect drift, and ensure stability.

   

Requirements

• 5+ years of experience in machine learning or deep learning.
• Strong Python skills and hands-on experience with PyTorch, TensorFlow, or JAX.
• Solid understanding of optimization algorithms and deep learning principles.
• Experience with cloud platforms (AWS, GCP, or Azure) and MLOps practices.
• Familiarity with LLMs, prompt engineering, and generative AI.
• English — Upper-Intermediate level or higher.

Nice to Have

• Experience with distributed training, model compression, or inference optimization.
• Knowledge of ONNX, Triton, or Evidently AI.
• Experience with diffusion models, GANs, or gr