Jr-mid level part-time consulting opportunity

About Us

We are partnering with a global digital transformation team supporting next-generation AI initiatives for a major enterprise technology program. This role will work directly alongside a senior AI architect helping validate model accuracy, support training workflows, and contribute to the development and refinement of production-bound machine learning solutions within GCP. This is an excellent opportunity for an early-career AI/ML engineer to gain hands-on experience with real-world model evaluation, LLM development workflows, and enterprise-scale applied AI systems.

Why join us?

• Part-time contract (20 hours per week)
• Fully remote
• Flexible scheduling
• Opportunity to contribute to high-visibility enterprise AI development initiatives

Job Details

Responsibilities

• Support evaluation and validation of machine learning and LLM outputs for accuracy, consistency, and reliability within GCP
• Assist with training dataset preparation, labeling, and refinement
• Contribute to prompt engineering experimentation and response testing
• Help monitor model performance and recommend improvements
• Collaborate with senior engineers on iterative model tuning and deployment readiness
• Document workflows, testing results, and evaluation methodologies
• Participate in ongoing experimentation supporting production AI solutions

Required Qualifications

• 1-3 years of experience in Machine Learning, AI engineering, or applied data science
• Hands-on experience with Python for ML workflows
• Professional experience working in GCP (Google Cloud Platform) required
• Familiarity with LLM ecosystems (OpenAI, Gemini, Claude, or similar)
• Experience working with structured and unstructured datasets
• Exposure to model validation, testing pipelines, or evaluation frameworks
• Strong attention to detail and analytical problem-solving skills
• Ability to work independently in a distributed engineering environment

Preferred Qualifications

• Experience supporting prompt engineering or RAG-style workflows
• Exposure to model fine-tuning or dataset optimization techniques
• Familiarity with evaluation tooling for LLM accuracy and hallucination detection
• Experience working alongside senior architects or research teams
• Background supporting enterprise AI initiatives or production-bound experimentation