Staff Machine Learning Engineer Job at AskElephant, Lehi, UT

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  • AskElephant
  • Lehi, UT

Job Description

About the Role

We are looking for a Staff ML Engineer to build and optimize AI-driven data extraction and standardization systems . As we scale, ensuring our AI agents can reliably extract, structure, and standardize data across diverse sources is critical. This role will focus on developing a multi-agent AI system where specialized agents work together to:

  • Extract & Standardize Data from structured and unstructured sources with high accuracy.
  • Optimize Multi-Agent Interactions to improve extraction workflows and minimize inconsistencies.
  • Enhance Performance & Scalability through fine-tuning, prompt optimization, and intelligent workflow design.
  • Implement MLOps Best Practices for monitoring, deployment, and continuous improvement of AI models.

This is an in-office role, where you will collaborate closely with ML researchers, data engineers, and product teams to build robust AI-powered solutions.

Responsibilities

  • Design & Optimize a Multi-Agent AI System for structured data extraction and standardization using A2A and MCP.
  • Fine-Tune LLMs & Optimize Prompts to enhance extraction accuracy and contextual understanding.
  • Develop Intelligent Workflows that improve agent coordination, response quality, and feedback loops .
  • Implement Performance Optimization Strategies to improve latency, accuracy, and cost-efficiency .
  • Automate AI Model Evaluation using response quality metrics and continuous learning techniques.
  • Deploy & Monitor AI Models with MLOps tools for logging, monitoring, and automated retraining.
  • Ensure Model Reliability & Explainability by implementing robust monitoring and debugging workflows.
  • Work Closely with Data & Engineering Teams to ensure seamless integration into business workflows.

Requirements

  • 5+ years of experience in machine learning, deep learning, or AI engineering.
  • Strong expertise in LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG), Model Context Protocol (MCP), and agent2agent (A2A) .
  • Experience with multi-agent AI architectures, model optimization, and distributed AI systems .
  • Proficiency in Python, TensorFlow, PyTorch, and Hugging Face .
  • Hands-on experience with LLM evaluation, response ranking, and feedback-driven model improvements .
  • Familiarity with Google Cloud AI tools, BigQuery, and scalable ML infrastructure .
  • Strong understanding of data extraction, entity recognition, schema mapping, and data validation techniques .
  • Experience in orchestrating ML workflows using tools like Ray, Airflow, or LangGraph .
  • MLOps Experience: Expertise in monitoring, logging, and automating ML model deployment with tools like MLflow, Kubeflow, Vertex AI, or SageMaker .
  • Understanding of vector databases and efficient retrieval mechanisms .

Nice to Have

  • Experience with real-time AI inference and event-driven architectures .
  • Exposure to LLMOps, MLOps, and ML architectures supporting AI/ML workflows.
  • Proficiency with Node and Typescript.

What We Offer

  • Competitive salary & benefits .
  • Collaborative in-office work environment .
  • Opportunity to work with cutting-edge AI and ML technologies .
  • Exciting challenges & rapid growth opportunities .
  • Continuous learning & professional development .

🚀 If you're passionate about building scalable AI systems, optimizing LLM performance, and integrating MLOps best practices, we’d love to hear from you! Apply now.

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