Junior AI Engineer

Plan Group•Macomb County, MI

10h

About The Position

The Junior AI Engineer builds practical AI-enabled applications and workflows across finance, procurement, operations, and IT. The role works with business and technical specialists to connect AI services, enterprise data, and software into useful, reliable solutions. As an early-career position with hands-on ownership, it involves building, testing, releasing, and improving solutions, with support from experienced specialists on business requirements, architecture, security, and integration.

Requirements

Exposure to Azure development, such as Azure Functions, Azure OpenAI / Microsoft Foundry, Azure AI Speech, or Azure SQL; equivalent cloud or backend development experience is also valuable.
Experience using common AI and software development environments such as Jupyter Notebook, Git, Linux, or cloud-based development tools.
Experience with exact lookups, aliases, keyword or fuzzy matching, candidate ranking, and ambiguity handling.
Experience deploying a working application or API, using authentication and secure service connections, and improving reliability through tests, logs, or monitoring.
Coursework or projects involving machine learning, data engineering, software architecture, or responsible AI.

Nice To Haves

Semantic or vector search experience where simpler retrieval is insufficient.
Exposure to deployment pipelines.
Experience integrating with enterprise systems or working with finance, procurement, construction, project, or operational data, including reference or master data and controlled refreshes.

Responsibilities

Own the technical design and development of AI solutions from an identified business problem through prototype, testing, and controlled release.
Work with business and application specialists to understand processes, rules, data, and exceptions, and determine how to implement them using AI, software, integrations, and deterministic logic.
Select and apply suitable techniques and tools, including speech-to-text integration, structured LLM outputs, search, retrieval, matching, and model evaluation; use deterministic code when authoritative rules or data apply.
Build maintainable user-facing applications and backend services that retrieve trusted records, manage draft state, handle ambiguity and corrections, and require confirmation before generating business output.
Develop and consume APIs to connect solutions to enterprise applications and trusted data using JSON, SQL, and approved integration mechanisms; handle errors explicitly and generate files against verified interface specifications.
Separate AI interpretation from trusted-system validation and deterministic business rules, and implement authentication, authorization, secure credential handling, data permissions, logging, and version-bound human confirmation within enterprise standards.
Build automated tests for realistic scenarios, ambiguous inputs, corrections, and failure paths; debug across application, AI, database, and API boundaries; and evaluate accuracy and safe handling of unresolved information.
Work iteratively with users, incorporate feedback, prepare controlled releases, and measure whether solutions save effort, improve quality, or reduce cycle time.
Use logging and monitoring to improve reliability after release.
Create repeatable tests, technical notes, and support documentation, and contribute reusable code and patterns.