Job Role: AI Engineer-MODS
Location: Remote
Employement Type: No C2C only W2
Job Description:
Our client, a global leader in telecommunications technology, is looking for an AI Engineer to help power one of the industry's most ambitious AI transformation initiatives. This is your chance to build agentic AI systems that are changing how a telecom company designs and optimizes its RF tower network, moving from manual engineering workflows to intelligent, autonomous execution.The end-client is embedding AI agents directly into its newly built RFDA platform to automate critical parts of the RF tower design (RFDS) process. You will help build agentic workflows that analyze engineering requests, run RF simulations, and generate optimized design recommendations, all while supporting a human-in-the-loop model that evolves toward full autonomy.This is a high-visibility project with a clear roadmap and real deployment milestones. You will see your work go from pilot to production in a matter of months, with direct exposure to how large-scale telecom infrastructure decisions get made.
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.
Take the lead during 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.
Required Skills:
4-7 yrs production Python; at least 1 yr 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 a new KB partition when one already exists.
Reads CLI / scripting API contracts cleanly and instruments them with structured logging + run-context.
Preferred Skills:
MCP server contributor (extending, not just consuming).
Test discipline - pyTest, fixtures for synthetic external systems, golden-scenario regression patterns.
RF / telecom adjacency.
Best Regards:
Sophia Sinclair
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