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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