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).

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