AI Application Engineer

IS International Services

About The Position

The AI Application Engineer acts as a "Force Multiplier" for the engineering and operations teams. Reporting to the CTO (Chief Technology Officer), this role bridges the gap between hardware design, software execution, and field delivery. You will be responsible for building practical, high-impact automation tools that eliminate manual drudgery. This includes developing AI-assisted workflows (RAG pipelines), automating CAD/BOM generation, and creating intelligent field support agents. This role is for a pragmatic builder who enjoys turning messy, real-world engineering processes into streamlined software solutions.

Requirements

Core Coding: Strong proficiency in Python. Familiarity with APIs (REST), file parsing, SQL, and data structuring (JSON/XML/Pandas).
AI & LLMs: Demonstrated experience building practical applications using LLMs (OpenAI API, Anthropic, LangChain) and vector databases. Understanding of prompt engineering and hallucination mitigation.
Document Pipelines: Experience with programmatic document generation (templating engines, LaTeX, PDF generation, or Docs-as-Code).
Domain Fluency: Ability to read Single Line Diagrams (SLDs) and understand fundamental power system terminology (kV, MW, MWh, Transformer, Inverter).
Hardware Awareness: Familiarity with basic electrical components (cables, switchgear, protection relays) allows you to "speak the language" of the engineers you are supporting.
The Translator: Ability to listen to a Mechanical or Electrical Engineer describe a bottleneck and translate that into a software requirement.
Pragmatic Innovator: You view AI as a utility tool, not a magic wand. You prioritize safety, accuracy, and utility over hype.
Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or Mechanical Engineering.
0–2 years of experience (Internships and Capstone projects count).
Must be able to demonstrate or discuss a project where you used software/AI to solve a real-world workflow problem.

Nice To Haves

CAD Automation: Exposure to scripting within CAD environments (AutoCAD, SolidWorks, or Revit APIs) is a significant plus.

Responsibilities

Workflow Scripting: Develop Python-based tools to automate the generation of 2D installation drawings (GA layouts, conduit entries) and site-specific Bills of Materials (BOMs) to reduce human error.
Technical Assistants (RAG): Build and maintain "Design Standard" chatbots utilizing Retrieval-Augmented Generation (RAG) to help engineers quickly query complex codes (UL 9540, NFPA 855, IEEE 693) with strict accuracy guardrails.
Logistics Optimization: Collaborate with logistics teams to build data-driven tools that optimize container weight and shipping configurations during the design phase.
Automation Playbooks: Develop process for generating Automation Playbooks for software and firmware deployment and configuration (e.g. using Ansible or similar technology) of on-premises systems.
Industrial System Code Generation: Develop process for generating Structured Text language from modbus maps for use with Codesys
Next-Gen Documentation: Move beyond static PDFs by creating pipelines that automatically assemble dynamic O&M manuals and commissioning checklists from engineering data sources.
Field Support Agents: Prototype and deploy "Q&A" agents that allow field technicians to query torque specs, installation procedures, and troubleshooting steps via natural language.
Feedback Loops: Create data pipelines that capture field evidence (logs, photos) and systematically feed that data back to the design team to improve future product iterations.
Digitized Commissioning: Build tooling to generate, validate, and track precommissioning checklists and power-up sequences.
Data Virtualization: Standardize fleet operational data to ensure it is formatted correctly for future machine learning analysis and model validation.
Performs other related duties as assigned.