Applied AI​/ML Engineer

Remote, US

Certum Legal Solutions (CLS) is a pioneering legal managed services organization (MSO) that supports mass-tort and single-event personal injury law firms. We provide the operational infrastructure, technology, and back-office solutions that allow the firms on our platform to grow and scale while focusing on delivering industry-leading results for their clients.

We are building a proprietary, AI-native system that will transform every dimension of how CLS and its platform firms operate. This system encompasses AI-powered agents to manage end-to-end workflows throughout the lifecycle of mass-tort and single-event personal-injury litigation matters, including back-office support functions such as HR, accounting and finance, and IT. This is a greenfield build on a modern stack, designed from Day 1 to be extensible and, eventually, productized for the broader legal services market.

We are assembling a high-caliber team to build something that does not exist in this form anywhere in the legal industry. If you want to see your work directly transform a real business and believe AI is fundamentally reshaping professional services, this is the team to join.

The Applied AI/ML Engineer owns the intelligence layer of Project Interstellar: the infrastructure that extracts structured data for robust document intelligence, powers conversational voice agents, orchestrates multi-step task agents, and handles various other operational workflows. This is not a research role. It is an applied AI/ML engineering role focused on building production LLM systems that perform reliably at scale in a HIPAA-regulated environment.

You will work directly with the Lead Backend Engineer to integrate AI capabilities into the platform architecture from the ground up.

• Production-grade structured data extraction pipeline for complex, multi-page domain-specific documents: entity extraction, classification, confidence scoring, temporal sequencing, and calculated output generation
• Model abstraction layer with task-based routing, enabling cost-optimized inference across workloads of varying complexity (from high-volume classification to deep multi-step reasoning)
• Agent orchestration framework and tool registry enabling task-executing agents to receive natural language instructions, plan multi-step executions, and call platform services
• Quality assurance agent layer: automated rubric-based verification of upstream agent outputs against defined accuracy and completeness standards
• Training and evaluation agent system: interactive scenario simulation and competency assessment for platform operators
• RAG layer over domain-specific internal knowledge bases, enabling intelligent lookup and context injection for operational agents

• Own all AI/ML system design and implementation on the platform
• Design and maintain prompt engineering infrastructure: versioned prompts, A/B evaluation, and regression testing
• Build and iterate on structured extraction pipelines for complex, regulated documents, from initial parsing through multi-step reasoning and output validation
• Design the agent orchestration framework powering task agents, QA agents, and operational agents
• Establish AI quality standards: confidence thresholds, human escalation triggers, output validation
• Partner with the Lead Backend Engineer on model abstraction layer design and API integration
• Partner with domain experts on extraction schema design and workflow logic

• 5+ years of software engineering experience with at least 2–3 years focused on production LLM application development
• Expert Python; strong API integration skills; experience deploying AI systems at production scale
• Deep familiarity with…