Senior AI/ML Engineer

Anvaya Solutions•Sacramento, CA

About The Position

This role involves a 1-year base term with the option to extend for two additional years. The Senior AI/ML Engineer will focus on leveraging artificial intelligence and machine learning to enhance software engineering processes, design advanced LLM systems, and implement predictive intelligence for system monitoring. The position requires a strong background in AI-enhanced software development, LLM/Agentic system design, predictive log and telemetry intelligence, and AI quality guardrails.

Requirements

Minimum five (5) years of experience in AI-Enhanced Software Engineering.
At least three (3) years of experience in Advanced Large Language Model (LLM) and Agentic System Design.
At least three (3) years of experience in Predictive Log and Telemetry Intelligence.
At least two (2) years of experience in AI Quality Guardrails.
Advanced skills in Python and strong knowledge of backend architecture.
Hands-on experience using AI coding assistants (such as GitHub CoPilot or Cursor).
Proven ability to set up and manage automated Continuous Integration/Continuous Deployment (CI/CD pipelines).
Experience implementing AI-driven quality checks and generating unit tests within CI/CD workflows.
Hands-on experience designing multi-agent workflows that coordinate tasks across different AI agents.
Building and optimizing Retrieval-Augmented Generation (RAG) pipelines, including improving vector database search.
Applying Chain-of-Thought reasoning techniques to support complex tasks in the Software Development Lifecycle (SDLC).
Using frameworks such as LangGraph, CrewAI, or AutoGPT to automate end-to-end development processes.
Working with large, unstructured datasets such as system logs, distributed traces, and heat dumps.
Designing solutions that can identify patterns and predict issues before they impact system performance.
Applying AI techniques to improve log analysis and telemetry monitoring for faster troubleshooting.
Building Human-in-the-Loop systems where humans review and validate AI outputs for accuracy and safety.
Creating automated evaluation frameworks such as Retrieval-Augmented Generation Assessment (RAGAS) and Generative Evaluation (G-Eval) to measure AI performance.
Implementing safeguards to prevent hallucinations (incorrect or fabricated outputs) in technical results.
Designing strategies to mitigate prompt-injection risks for autonomous AI agents, ensuring secure and reliable operations.

Nice To Haves

Experience leveraging LLMs to analyze Salesforce metadata, generate Apex code, and automate validation of complex SFDX deployment packages.
Experience in applying computer vision techniques for automated testing of frontend user interfaces (UI) and user experience (UX).
Experience in designing AI agents, capable of analyzing and mapping complex distributed system dependencies to support refactoring of legacy codebases.

Responsibilities

Enhance software engineering processes using AI.
Design and implement advanced Large Language Model (LLM) and Agentic Systems.
Develop solutions for predictive log and telemetry intelligence.
Implement AI Quality Guardrails for AI outputs.
Set up and manage automated CI/CD pipelines with AI-driven quality checks.
Design multi-agent workflows for task coordination.
Build and optimize Retrieval-Augmented Generation (RAG) pipelines.
Apply Chain-of-Thought reasoning techniques within the SDLC.
Use frameworks like LangGraph, CrewAI, or AutoGPT for process automation.
Work with large, unstructured datasets for pattern identification and issue prediction.
Improve log analysis and telemetry monitoring using AI.
Build Human-in-the-Loop systems for AI output validation.
Create automated evaluation frameworks for AI performance measurement.
Implement safeguards against hallucinations in technical results.
Design strategies to mitigate prompt-injection risks for AI agents.