Applied AI Engineer

Job Title: Applied AI Engineer
Location: Remote

Key Responsibilities:

• Design, develop, and fine-tune machine learning models for various AI applications.
• Work with business analysts to identify and gather the necessary information for AI/ML training datasets, ensuring proper preprocessing and feature extraction.
• Fine-tune open-source models on company-specific datasets to meet business needs.
• Implement and optimize model inference pipelines, ensuring efficiency and scalability.
• Build APIs around trained models for integration with various applications.
• Collaborate with data scientists, business analysts, software engineers, and other stakeholders to deploy models into production.
• Stay current with advancements in machine learning, AI, and open-source models, applying new techniques to improve existing models and develop innovative solutions.
• Troubleshoot and optimize model performance in both development and production environments.

Required Skills & Experience:

• Bachelor's or Master’s degree in Computer Science, Data Science, AI, or a related field.
• 3+ years of experience in machine learning, deep learning, or AI-focused development.
• Hands-on experience with open-source machine learning models (e.g., Hugging Face models, GPT, BERT, etc.) and fine-tuning them on custom datasets.
• Strong programming skills in Python, along with experience using ML frameworks such as PyTorch, TensorFlow, or Keras.
• Experience working with business analysts to define and gather data for training AI models.
• Experience with model inference, optimization, and building API layers for model integration.
• Familiarity with MLOps practices, including model versioning, deployment, and monitoring.
• Experience with data preprocessing techniques for structured and unstructured data (e.g., images, text, time-series data).
• Proficiency with cloud platforms (AWS, GCP, or Azure) for training, deployment, and scaling of ML models.
• Understanding of distributed training techniques and working with large datasets.

Nice to Have:

• Experience working with transfer learning or domain adaptation techniques.
• Knowledge of reinforcement learning or unsupervised learning approaches.
• Familiarity with containerization (Docker) and orchestration tools (Kubernetes).
• Experience in building and maintaining end-to-end MLOps pipelines.
• Understanding of transformers and other modern AI architectures.
• Familiarity with continuous integration and continuous deployment (CI/CD) for ML models.
• Contributions to open-source AI or machine learning projects.