Sr AI Data Engineer

As a Sr Data Engineer here at Honeywell, you will play a crucial role in designing and implementing advanced data solutions for AI solutions that drive business insights, enhance decision-making processes and empower AI solutions. Your expertise will help in critical data science development activities across all AI modalities (classic, Gen and agentic) and data types (structured and unstructured).

You will report directly to our AI Director and you’ll work out of our Phoenix, AZ or Charlotte, NC location on a Hybrid work schedule.

In this role, you will impact the organization by leveraging your technical skills to develop innovative data solutions that support strategic initiatives and improve operational efficiency.

KEY RESPONSIBILITIES

• Support end‑to‑end data needs for all AI modalities , including classic ML, GenAI/LLMs, and agentic AI systems.
• Build robust, scalable data pipelines for structured, semi‑structured, and unstructured data , including text, documents, images, audio, video, and logs.
• Develop feature engineering pipelines for classic ML, including feature extraction, transformation, and feature store management.
• Build and optimize GenAI and LLM data pipelines , including embedding generation, vectorization, chunking, metadata extraction, and document enrichment for RAG and context retrieval.
• Develop data ingestion and orchestration workflows that support agentic AI , including memory stores, event-driven pipelines, tool-use data flows, and real-time retrieval services.
• Design and implement advanced data solutions using AWS (S3, Glue, Lambda, EMR, Kinesis), Databricks (Spark, Delta Lake, Vector Search), and Dataiku to enable intelligent systems at scale.
• Implement data governance, quality, lineage, monitoring, and observability to support high-performance, trustworthy AI.
• Partner with data scientists, ML engineers, and AI product teams to deliver datasets for model development, fine‑tuning, evaluation, and production inference.
• Optimize pipelines for latency, cost, reliability, and throughput, ensuring AI systems—from batch ML to real-time agents—have the data they need.