Senior AI Technical Architect/Remote
Sacramento, CA
Duration: Long term
Mandatory Requirements for Senior AI Technical Architect
· 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.
Never miss an opportunity! Create an alert based on the job you applied for.
