AI Applications Engineer

Edmund Optics•Barrington, NJ

4d•Onsite

About The Position

Edmund Optics is seeking an early-career AI Applications Engineer to build AI-powered business applications that connect AI models with company data, tools, and human review. The role works directly with business stakeholders to understand workflows and turn business problems into working software, while partnering with analytics, IT, cybersecurity, and continuous improvement teams. The engineer owns scoped projects and develops, deploys, and improves AI-driven solutions for a global optics and photonics manufacturer. The position is based in Barrington, New Jersey. Initial work may include customer documentation workflows and demand forecasting applications. Applicants should include a resume and a brief description of an AI application they built; a GitHub repository, portfolio, demo, or project write-up is welcome but optional. A GitHub profile, portfolio, or relevant project link on the resume is also encouraged.

Requirements

Demonstrated initiative and follow-through, including moving work forward independently and using judgment about when to clarify or escalate.
Experience building a working AI application using external tools or APIs, and ability to explain personal contributions, how tool calls are executed, and how results and failures are checked.
Ability to write, explain, debug, and test software in at least one relevant programming language.
Working knowledge of APIs, data structures, databases, and version control.
Working knowledge of agent tool/function calling, structured outputs, retrieval and context management, and agent orchestration, including how MCP clients and servers connect AI applications to tools and data.
Excellent communication skills, including understanding business workflows and explaining technical choices to non-technical stakeholders.
Demonstrated habit of learning and experimenting with emerging AI capabilities, applying feedback, and explaining what a new approach improves and where it falls short.
U.S. citizenship or permanent residence.
Bachelor's degree in Computer Science or a related field.
No minimum years of professional experience is required; relevant AI application experience may come from employment, internships, research, coursework, or independent projects.
Ability to operate office equipment such as a copier; see details at close range; sit at a desk or PC for long periods; and work in an office setting.

Nice To Haves

Experience with production deployment, monitoring, SQL, or business system integrations.
Hands-on experience building or integrating MCP servers or evaluating agent workflows.
Experience with forecasting or other applied data methods.
Experience with AI data platforms, especially Snowflake.
Exposure to optics, photonics, precision manufacturing, or a related technical industry.
Experience with cloud platforms, especially Microsoft Azure, such as Azure OpenAI, Azure AI Foundry, or Azure Functions.

Responsibilities

Build applications connecting AI models with APIs, business data, and tools, including MCP servers and multi-step agent workflows.
Write, debug, test, and maintain application code and integrations; choose an AI, analytical, or conventional software approach appropriate to each problem.
Meet with business stakeholders to understand workflows, clarify requirements, and agree on practical measures of success.
Identify useful next steps, build small working prototypes, and improve them through testing and feedback.
Evaluate application behavior and AI outputs, including missing information, incorrect answers, tool failures, and cases requiring human review.
Collaborate with IT, analytics, cybersecurity, continuous improvement, and business owners to deploy and support reliable applications within company policies and AI governance.
Surface blockers and possible next steps, communicate progress, document decisions, and address issues after launch.
Measure business results and improve solutions over time.
Stay current on AI models, agent frameworks, tool integrations, and development practices; test promising capabilities and recommend adoption based on usefulness, reliability, cost, and maintainability.
Comply with federal, state, and company policies, procedures, and regulations.