GenAI Engineer

Senior GenAI / Machine Learning Engineer

Position Title: Senior GenAI / Machine Learning Engineer

Work Location: Fully Remote (EST working hours, with flexibility)

Position Type: Contract (Slated through April 2027, extension expected)

Team Structure: Individual Contributor within a 10-person project team for a Big 4 consulting firm

Role Overview

We are seeking three Senior GenAI / Machine Learning Engineers to join a high-impact technical team working on enterprise Generative AI projects. This role requires strong foundations in machine learning and data engineering, paired with hands-on expertise in building RAG pipelines and Agentic AI frameworks. You will work across diverse datasets, cloud environments, and data workflows to construct production-ready AI solution flows.

Key Responsibilities

• Design, build, and deploy Generative AI applications, specifically focused on RAG pipelines and Agentic AI workflows.
• Develop complex data workflows and transformation pipelines handling both structured and unstructured data.
• Utilize NLP techniques to extract valuable insights from unstructured data sources across multiple cloud platforms.
• Implement end-to-end machine learning models and frameworks using Python and SQL.
• Distinguish between standard process automation and true Agentic flows to architect optimal system solutions.
• Work with disparate data sources across cloud environments (AWS, Azure, or GCP).

Required Skills & Qualifications

• Generative AI & Agentic Frameworks: Hands-on experience developing RAG pipelines and building Agentic flows using open-source and closed-source models. Clear conceptual understanding of Agentic flows versus basic automation.
• Core Technical Stack: Advanced proficiency in Python and SQL for data analysis, data transformation, model development, and pipeline execution.
• Data Engineering & Workflow: Strong data transformation experience working with varied data sources across cloud providers.
• Data Types: Practical experience handling both structured and unstructured data, including NLP methods for data extraction.
• Machine Learning: Solid foundation in machine learning concepts and hands-on experience with standard ML frameworks (e.g., scikit-learn, XGBoost, LightGBM, Hugging Face).
• Cloud & Infrastructure: Familiarity with cloud platforms (AWS, Azure, or GCP), MLOps practices, and containerization tools (Docker/Kubernetes).
• Education & Experience: Bachelor’s degree required; 4+ years of hands-on data science or machine learning experience.

Preferred Qualifications

• Bachelor’s degree in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, or related field).