AI Workflow Engineer

Verizon•Irving, TX

1d•$81,000 - $155,000•Hybrid

About The Position

The Transformation Data Science team addresses high-impact, analytically driven projects focused within three core pillars: Forecasting, Involuntary Churn & Bad Debt and Credit optimization. In the role of AI Workflow Engineer, you will be developing and deploying autonomous AI agentic workflows to streamline complex financial planning and analytical processes. You will implement and optimize Retrieval-Augmented Generation (RAG) systems to provide accurate, context-aware insights for financial decision-making. You will integrate Large Language Models (LLMs) into existing financial data pipelines to enhance forecasting accuracy and reduce manual processing time. You will ensure all AI-driven financial workflows are auditable, explainable, and meet strict financial data governance and compliance standards. You will design and build autonomous agents using LLMs and applying advanced prompt engineering techniques to solve complex financial problems. You will also build machine learning models for forecasting key financial metrics such as revenue, EBITDA, and other financial indicators.

Requirements

Bachelor's degree or four or more years of work experience.
Four or more years of relevant experience required, demonstrated through one or a combination of work and/or military experience, or specialized training.
Four or more years of experience in AI/ML application development.
Strong experience in building and deploying autonomous AI agentic workflows and RAG pipelines.
Proficiency in modern Python-based AI development, including hands-on experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, Crew AI) and vector databases.
Demonstrated ability to translate complex financial business logic into robust, scalable, and auditable AI architectures.

Nice To Haves

Proven strong understanding of data governance, model explainability, and compliance standards within financial systems.
Knowledge of big data tools (SQL, Spark, and Splunk), i.e., manipulating, mining data from these systems.
Work experience in descriptive, predictive, and prescriptive analytics in financial area.
Experience in augmenting, fine tuning LLM solutions.
Excellent interpersonal, verbal, and written communication skills, with the ability to thrive in a fast-paced, collaborative environment.

Responsibilities

Developing and deploying autonomous AI agentic workflows to streamline complex financial planning and analytical processes.
Implementing and optimizing Retrieval-Augmented Generation (RAG) systems to provide accurate, context-aware insights for financial decision-making.
Integrating Large Language Models (LLMs) into existing financial data pipelines to enhance forecasting accuracy and reduce manual processing time.
Ensuring all AI-driven financial workflows are auditable, explainable, and meet strict financial data governance and compliance standards.
Designing and building autonomous agents using LLMs and applying advanced prompt engineering techniques to solve complex financial problems.
Build machine learning models for forecasting key financial metrics such as revenue, EBITDA, and other financial indicators.