AI Technical Architect/Remote

· A minimum of five (5) years of experience in AI-Enhanced Software Engineering. Experience shall include at least three (3) of any of the following:

o Advanced skills in Python and strong knowledge of backend architecture.

o Hands-on experience using AI coding assistants (such as GitHub Copilot or Cursor) to speed up software development.

o Proven ability to set up and manage automated Continuous Integration/Continuous Deployment (CI/CD) pipelines.

o Experience implementing AI-driven quality checks and generating unit tests within CI/CD workflows.

· A minimum of three (3) years of experience in Advanced Large Language Model (LLM) and Agentic System Design. Experience shall include at least three (3) of any of the following:

o Hands-on experience designing multi-agent workflows that coordinate tasks across different AI agents.

o Building and optimizing Retrieval-Augmented Generation (RAG) pipelines, including improving vector database search for faster and more accurate results.

o Applying Chain-of-Thought reasoning techniques to support complex tasks in the Software Development Lifecycle (SDLC).

o Using frameworks such as LangGraph, CrewAI, or AutoGPT to automate end-to-end development processes.

· A minimum of three (3) years of experience in Predictive Log and Telemetry Intelligence. Experience shall include at least three (3) of any of the following:

o Building parsers and classifiers powered by Large Language Models (LLMS).

o Working with large, unstructured datasets such as system logs, distributed traces, and heap dumps.

o Designing solutions that can identify patterns and predict issues before they impact system performance.

o Applying AI techniques to improve log analysis and telemetry monitoring for faster troubleshooting.

· A minimum of two (2) years of experience in AI Quality Guardrails. Experience shall include at least three (3) of any of the following:

o Building Human-in-the-Loop systems where humans review and validate AI outputs for accuracy and safety.

o Creating automated evaluation frameworks such as Retrieval Augmented Generation Assessment (RAGAS) and Generative Evaluation (G-Eval) to measure AI performance.

o Implementing safeguards to prevent hallucinations (incorrect or fabricated outputs) in technical results.

o Designing strategies to mitigate prompt injection risks for autonomous AI agents, ensuring secure and reliable operations.