Lead AI and Data Engineer

Eaton Corporation•Beachwood, OH

19h•$97,000 - $143,000•Remote

About The Position

Eaton’s Corporate Sector division is currently seeking a Lead AI and Data Engineer. As a Lead AI & Data Engineer, you will design, develop, and deploy scalable AI-powered solutions that drive business value across the enterprise. You'll work across the technology stack, combining software engineering best practices with modern AI and machine learning capabilities to deliver secure, reliable, and innovative solutions.

Requirements

Bachelor's degree from an accredited institution
Minimum 3 years of experience in software engineering
Only candidates residing within a 50 mile radius of Beachwood, OH; or Mountainside, NJ area will be considered. Active Duty Military Service member candidates are exempt from the geographical area limitation.
Eaton will not consider applicants for employment immigration sponsorship or support for this position. This means that Eaton will not support any CPT, OPT, or STEM OPT plans, F-1 to H-1B, H-1B cap registration, O-1, E-3, TN status, I-485 job portability, etc.

Nice To Haves

Experience utilizing best practices in software engineering
Experience developing enterprise grade, highly scalable web-based applications and/or distributed systems
Strong knowledge of machine learning algorithms and principles.
Proficiency with Azure AI technologies including Azure Open AI
Experience with Snowflake is a plus
Strong programming skills, preferably in Python, experience with C# and .NET is a plus.
Solid understanding of software design principles, algorithms, data structures, and multithreading concepts
Solid understanding of DevSecOps, CI, and CD principles from code check-in through to deployment
Experience with modern software development principles including code management, test automation, APIs, microservices, and cloud services.
Experience working with Agile, Scrum, or Kanban

Responsibilities

Author high quality, unit tested code in an iterative manner
Drive a Secure Product Development Lifecycle approach that establishes a strong cybersecurity focus and culture across the entire engineering lifecycle
Possess good knowledge of a wide range of technologies and programming languages. Maintain job knowledge by studying software development techniques and programming languages. Participate in educational opportunities and read professional publications.
Develop, build, and configure solutions that implement user stories
Develop and execute agile work plans for iterative and incremental product delivery
Ensure that solutions meet technical requirements, non-functional requirements, and enterprise technology standards
Continuously integrate and deploy solutions (with support of DevOps and service teams)
Test software to ensure responsiveness and performance. Work with test teams to ensure adequate and appropriate test case coverage; investigate and fix bugs; create automated test scripts.
Maintain, operate, and monitor solutions
Create prototype designs for a product very rapidly using a wide range of techniques
Demonstrate and document solutions by using flowcharts, diagrams, code comments, code snippets, and performance instruments
Recommend software tools to management and architecture teams
Implement and maintain the infrastructure needed for end-to-end machine learning workflows including data collection, model training, and deployment in production environments.
Manage the lifecycle of large language models including training, evaluation, deployment, and monitoring of these models.
Evaluate the performance of various machine learning models using appropriate metrics and statistical tests. Make recommendations on which models to use based on their performance.
Fine-tune and enhance existing Retrieval Augmented Generation (RAG) and LLM models to improve their performance and adaptability.
Design and optimize prompts to effectively guide the behavior of language models. Understand and manage the flow of prompts in a conversation or task.
Provide guidelines and strategies for improving the accuracy of machine learning models.
Advise on how to optimize token usage to save costs and improve performance including strategies for efficient data preprocessing, model architecture design, and deployment.
Leverage Azure AI technologies for developing, deploying, and managing AI solutions with Azure Machine Learning and Cognitive Services.
Have knowledge and experience in deploying models and applications in cloud environments, particularly Azure.