Job Title: AI Engineer - MODS
Location: Remote
Company Overview
Glint Tech Solutions is a women-owned, global IT staffing and recruiting firm supporting enterprise clients across the USA and Canada.
Project Description
A leading enterprise client in the telecommunications technology space is looking for an AI Engineer to power one of the industry's most ambitious AI transformation initiatives — building agentic AI systems that are changing how the company designs and optimizes its RF tower network, moving from manual engineering workflows to intelligent, autonomous execution. The client is embedding AI agents directly into its newly built RFDA platform to automate critical parts of the RF tower design (RFDS) process. This role will help build agentic workflows that analyze engineering requests, run RF simulations, and generate optimized design recommendations, supporting a human-in-the-loop model that evolves toward full autonomy. This is a high-visibility project with a clear roadmap, taking work from pilot to production within months.
Key Responsibilities
• Implement bounded MOD scenario wrappers (5G NR add, LTE-NR / C-band, carrier add/remove, SNAP) inside the existing RFDS Agent (extends, does not fork)
• Co-author unit + integration tests against per-region golden MOD scenarios
• Support late-Sep MODS soft pilot (1-2 Champions) — instrument, observe, triage
• Lead the Oct Champions reverse-shadow for MODS and Nov wider rollout
• Reinforce NB during cutover week (Aug 31 - Sep 5) and Sep hypercare as a second pair of hands
Mandatory Skills
• 4-7 years of production Python experience; at least 1 year on LLM / agentic systems in production or near-production
• Hands-on Google Vertex AI or transferable agent framework experience (Agent Builder, LangGraph, Bedrock Agents), demonstrated by shipped work
• Comfortable extending an existing agent codebase rather than inventing one — disciplined enough to not spin up a new MCP server or KB partition when one already exists
• Ability to read CLI / scripting API contracts cleanly and instrument them with structured logging + run-context
Nice-to-Have Skills
• MCP server contributor (extending, not just consuming)
• Test discipline — pytest, fixtures for synthetic external systems, golden-scenario regression patterns
• RF / telecom adjacency