Senior AI/ML Engineer
Total Performance Consulting
Senior AI/ML Engineer
Total Performance Consulting
Governed AI and senior engineering services
As the Sr AI/ML Engineer, you’ll lead the development of our core machine learning systems. You’ll fine-tune large language models (LLMs), build NLP pipelines, automate insurance workflows, and implement user feedback loops for continual model improvement.
This is a senior role for someone who’s not afraid of ambiguity, gets energy from building things that don’t exist yet, and wants to work on meaningful problems with a small, fast-moving team.
You’ll also collaborate with the founders (CEO & CTO) to help define the company’s technical direction.
Responsibilities:
Natural Language Processing & Understanding: Build and fine-tune NLP systems to extract structured data and insights from dense, jargon-heavy insurance documents.
Lead the development and fine-tuning of large language models tailored to our domain.
Architect training workflows that use user feedback and corrections to continually improve model performance.
Apply computer vision to extract data from PDFs, scanned documents, and other unstructured formats.
Build intelligent agents that interact with complex web interfaces using tools like Playwright or Selenium, and a strong understanding of DOM structures.
Design robust evaluation pipelines and performance metrics for all models.
Help architect scalable systems to support training, inference, and feedback pipelines.
You’ll be building core systems from scratch.
Qualifications:
6+ years with Python development experience
4+ years experience as an AI/ML Developer.
JavaScript / Node.js / React
Experience with Mongo DB and AWS
Upper Intermediate English level
Your partner for AI, consulting, software development, and nearshore staffing.
Governed AI and senior engineering services
Perform delivers governed AI and senior engineering services for mission-critical systems, combining consulting, staff augmentation, and AI delivery to implement AI, modernize legacy software, and assure quality for AI-powered products. Its Governed AI approach keeps AI transparent and auditable, with a human engineer accountable for every output, and its distributed team of about 100 engineers works in client time zones and integrates with existing teams. This differentiates it from competitors through explicit human accountability, long-term self-funding, and end-to-end services tailored to enterprise needs. Its goal is to enable safe, auditable, and dependable AI within enterprise workflows so organizations can adopt AI in critical operations without sacrificing reliability.
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