AI/ Machine Learning Engineer
Role: Fractional AI/ Machine Learning Engineer
At Humiint, we’re helping companies unlock the power of AI by connecting them with trusted, on-demand experts—strategic, technical, and ready to deliver. Our platform matches forward-thinking organizations with seasoned professionals across AI, data, engineering, compliance, finance, and beyond.
We’re expanding our technology network and are actively seeking experienced Fractional AI / Machine Learning Engineers to support clients in building, deploying, and scaling intelligent systems. From GPT fine-tuning to recommendation engines, you’ll help shape real-world solutions with cutting-edge machine learning techniques. This role is perfect for AI professionals who want to work flexibly across a variety of projects, industries, and use cases—while having full control over their schedule and scope.
Your responsibilities may include:
• Designing and deploying machine learning models (classification, regression, NLP, generative AI, etc.)
• Fine-tuning or adapting foundation models (e.g., GPT, LLaMA, BERT) for client-specific applications
• Building data pipelines, training workflows, and automated evaluation metrics
• Collaborating with product, engineering, and data teams to integrate AI into digital products and services
• Developing MLOps infrastructure for versioning, deployment, monitoring, and retraining
• Running experiments to evaluate model performance, fairness, and interpretability
• Staying up-to-date with AI research trends, tooling, and ethical best practices
Required qualities may include:
• Proven experience in applied machine learning, deep learning, or AI engineering
• Proficiency with Python and ML frameworks such as TensorFlow, PyTorch, scikit-learn, or Hugging Face
• Experience with NLP pipelines, generative models (LLMs), or computer vision applications
• Strong understanding of data preprocessing, feature engineering, and model evaluation techniques
• Familiarity with MLOps tools like MLflow, Airflow, SageMaker, or Weights & Biases
• Ability to translate business problems into AI-powered solutions
• Clear, collaborative communication skills—especially in remote and cross-functional environments
• Experience working with vector databases, retrieval-augmented generation (RAG), or AI agents
Additional Information
• Ability to set your own pricing
• Remote work
• Flexible schedule
Role: Fractional AI/ Machine Learning Engineer
At Humiint, we’re helping companies unlock the power of AI by connecting them with trusted, on-demand experts—strategic, technical, and ready to deliver. Our platform matches forward-thinking organizations with seasoned professionals across AI, data, engineering, compliance, finance, and beyond.
We’re expanding our technology network and are actively seeking experienced Fractional AI / Machine Learning Engineers to support clients in building, deploying, and scaling intelligent systems. From GPT fine-tuning to recommendation engines, you’ll help shape real-world solutions with cutting-edge machine learning techniques. This role is perfect for AI professionals who want to work flexibly across a variety of projects, industries, and use cases—while having full control over their schedule and scope.
Your responsibilities may include:
• Designing and deploying machine learning models (classification, regression, NLP, generative AI, etc.)
• Fine-tuning or adapting foundation models (e.g., GPT, LLaMA, BERT) for client-specific applications
• Building data pipelines, training workflows, and automated evaluation metrics
• Collaborating with product, engineering, and data teams to integrate AI into digital products and services
• Developing MLOps infrastructure for versioning, deployment, monitoring, and retraining
• Running experiments to evaluate model performance, fairness, and interpretability
• Staying up-to-date with AI research trends, tooling, and ethical best practices
Required qualities may include:
• Proven experience in applied machine learning, deep learning, or AI engineering
• Proficiency with Python and ML frameworks such as TensorFlow, PyTorch, scikit-learn, or Hugging Face
• Experience with NLP pipelines, generative models (LLMs), or computer vision applications
• Strong understanding of data preprocessing, feature engineering, and model evaluation techniques
• Familiarity with MLOps tools like MLflow, Airflow, SageMaker, or Weights & Biases
• Ability to translate business problems into AI-powered solutions
• Clear, collaborative communication skills—especially in remote and cross-functional environments
• Experience working with vector databases, retrieval-augmented generation (RAG), or AI agents
Additional Information
• Ability to set your own pricing
• Remote work
• Flexible schedule
