AI Automation Engineer at Hyper Solutions Inc

Richmond, Virginia, United States -

Full Time

Start Date

Immediate

Expiry Date

29 Jul, 26

Salary

0.0

Posted On

30 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Generative AI, LLMs, Prompt Engineering, RAG, ETL Pipelines, Data Science, Automation Frameworks, MLOps, Process Mining, ERP Integration, CRM Integration, Data Preparation, Agent Orchestration

Industry

Electrical Equipment Manufacturing

Description

Description The AI Automation Engineer at Hyper Solutions plays a key role in designing and deploying AI-powered automation systems that improve how the business operates across sales, operations, supply chain, and customer-facing workflows. This position focuses on transforming manual processes into intelligent, scalable systems using machine learning, generative AI, and automation frameworks. This role operates at the intersection of data science, software engineering, and operational improvement. You will build and deploy automation pipelines, integrate AI capabilities into core business systems, and deliver measurable impact through efficiency gains, improved decision-making, and enhanced user experiences. This position is well-suited for an engineer who enjoys applying AI and automation to real-world business problems, thrives in ambiguity, and is comfortable owning solutions from discovery through production deployment. Core Responsibilities Analyze business processes across sales, operations, and supply chain to identify automation opportunities with measurable impact Design, build, and deploy AI-powered automation solutions across internal and customer-facing workflows Own the full lifecycle of automation systems including data preparation, model development, deployment, and monitoring Develop and maintain LLM-based pipelines including prompt engineering, RAG workflows, and agent orchestration Build integrations across enterprise systems such as ERP, PLM, CRM, and service platforms to enable AI-driven workflows Design and implement ETL pipelines to support data-driven automation and decision systems Apply appropriate tools and techniques including machine learning, generative AI, and process automation based on problem context Deploy process mining capabilities to improve visibility into workflows and identify automation opportunities Define and track automation KPIs such as cycle time reduction, error rate, and operational efficiency improvements Translate business requirements into scoped automation solutions with clear success criteria Additional Responsibilities Evaluate and recommend tools, platforms, and technologies to expand Hyper’s automation capabilities Contribute to building a scalable automation framework across business units Partner with stakeholders to embed automation outputs into existing tools and workflows Develop and deliver internal AI training programs to improve organizational adoption and capability Maintain documentation for models, workflows, and automation systems Requirements 3+ years of experience in data science, machine learning, or automation engineering roles Strong proficiency in Python for data processing, model development, and automation workflows Experience building and deploying machine learning or AI-driven systems in production environments Hands-on experience with LLMs, prompt engineering, or generative AI frameworks Experience designing and implementing data pipelines and ETL processes Familiarity with MLOps practices including model versioning, deployment, and monitoring Experience integrating with enterprise systems such as ERP, CRM, or similar platforms Ability to take projects from initial concept through deployment and production support Preferred Qualifications Experience with agent frameworks such as LangChain, AutoGen, or similar Familiarity with process mining tools or workflow analysis platforms Experience working with enterprise BI tools such as Tableau or Power BI Exposure to manufacturing, industrial, or data center environments Experience designing internal AI training or upskilling programs Familiarity with low-code or no-code automation platforms What Success Looks Like Manual workflows are replaced with scalable, AI-driven automation systems Business teams gain faster insights and improved decision-making capabilities Automation solutions deliver measurable improvements in efficiency and accuracy AI capabilities are seamlessly integrated into existing tools and workflows Automation systems are reliable, well-documented, and maintainable over time Why Hyper? At Hyper, you’ll play a key role in embedding AI and automation into the core of how the business operates. You’ll work across teams to deliver intelligent systems that improve efficiency, enhance decision-making, and drive innovation across a fast-growing organization. Hyper offers a collaborative and fast-paced environment where you can take ownership of impactful projects, work with modern AI technologies, and help shape how automation transforms industrial and operational workflows. Location Hybrid in Richmond, VA Hyper Solutions is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, veteran status, or any other protected status under applicable laws. All applications will be used exclusively for selection purposes and handled confidentially by authorized personnel only. Your application may also be considered for other suitable positions within Hyper Solutions, Inc. Please note that Hyper Solutions is currently unable to offer visa sponsorship, and applicants must be authorized to work in the U.S. without the need for sponsorship now or in the future.

Responsibilities

The AI Automation Engineer will design and deploy AI-powered automation systems to optimize sales, operations, and supply chain workflows. They will manage the full lifecycle of these systems, including data preparation, model development, and integration with enterprise platforms.