Fractional Machine Learning Engineer

About Fractional Machine Learning Engineer at Humiint

At Humiint, we partner with innovators and tech-forward companies to transform great data into deployed products. This role bridges the gap between prototype and full-scale production.

Key Responsibilities

• Model Deployment: Wrap models in APIs and containerize for scalable production.
• Pipeline Architecture: Build end-to-end data pipelines for training and inference.
• LLM Fine-Tuning: Adapt foundation models using LoRA/QLoRA techniques.
• Infrastructure Management: Configure cloud environments to optimize performance and cost.
• Latency Optimization: Reduce inference time using quantization and model distillation.
• Monitoring & Observability: Set up tools to detect data drift and maintain accuracy.
• Vector Database Implementation: Manage vector stores for RAG applications.

Required Qualities

• Proven experience as an ML, MLOps, or Backend Engineer with a deep data focus.
• Expert-level Python skills, with familiarity in C++ or Go.
• Deep experience with PyTorch, TensorFlow, Hugging Face Transformers, and LangChain.
• Proficiency with major cloud providers and their AI services.
• Commitment to version control, testing, and CI/CD principles.
• Ability to communicate technical trade-offs to non-technical stakeholders.

Additional Information

• Set your own pricing
• Virtual/Work from home
• Flexible schedule
• Opportunity to work across multiple industries

Key skills/competency

• Machine Learning
• MLOps
• Python
• Cloud
• API
• Containerization
• LLM
• CI/CD
• Pipeline
• Latency