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).