Machine Learning Engineer

Role:

Fractional Machine Learning Engineer

At Humiint, we partner with innovators and tech-forward companies who have great data but struggle to turn it into a deployed product.

Whether it's taking a prototype recommendation engine and scaling it to handle 10k requests per second, setting up an MLOps pipeline to retrain models automatically, or fine-tuning an open-source LLM for a specific vertical, the right Fractional Machine Learning Engineer bridges the gap between "it works on my laptop" and "it works in production"—without the overhead of a full-time AI Infrastructure Team.

We're looking to connect with builders and systems thinkers who know that the model is only 5% of the code.

If you are an expert at containerization, cloud inference, and optimizing latency, this is a great opportunity to apply your engineering rigor flexibly while building the intelligence layer for growing companies.

This role is ideal for MLOps practitioners and software engineers with a deep understanding of AI who want to focus on the architecture and deployment of machine learning systems on a part-time, project-based, or advisory basis.

Key responsibilities may include:

Model Deployment:

Wrapping models in APIs and containerizing them for scalable production use.

Pipeline Architecture:

Building end-to-end data pipelines that feed data into models for training and inference.

LLM Fine-Tuning:

Adapting foundation models using techniques like LoRA/QLoRA on custom datasets.

Infrastructure Management:

Configuring cloud environments to balance performance and cost.

Latency Optimization:

reducing inference time through quantization, model distillation, or using specialized hardware.

Monitoring & Observability:

Setting up tools to detect "data drift" and ensure model accuracy doesn't degrade over time.

Vector Database Implementation:

Setting up and managing vector stores for RAG applications.

Required qualities may include:

Proven Experience:

Background as an ML Engineer, MLOps Engineer, or Backend Engineer with a heavy data focus.

Code Fluency:

Expert-level Python skills and familiarity with C++ or Go for performance-critical components.

Framework Mastery:

Deep experience with PyTorch, TensorFlow, Hugging Face Transformers, and LangChain.

Cloud Native:

proficiency with the major cloud providers and their specific AI services.

Engineering Rigor:

A commitment to version control, testing, and CI/CD principles—treating ML assets like software code.

Pragmatism:The ability to choose the "boring," reliable solution over the newest, shiniest paper on arXiv when business stability is the goal.

Communication:

Explaining infrastructure costs and technical trade-offs to non-technical stakeholders.

Additional Information

Ability to set your own pricing

Virtual / Work from home

Flexible schedule

Work across industries

At Humiint, we work with trusted professionals who bring structure, scale, and reliability to the intelligence they build. If you're a Fractional Machine Learning Engineer ready to turn experiments into engines, we'd love to hear from you.

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