Senior Software Engineer - AI, CoCounsel FDE

Job Description

Are you a hands-on AI engineer who wants to build the next generation of legal AI solutions from the ground up? Do you thrive taking an architectural blueprint and turning it into a robust, production-ready system that lawyers actually rely on? Join Thomson Reuters' CoCounsel Forward Deployed Engineering team — a high-impact group working at the frontier of legal AI, where your code ships directly to law firms and corporate legal departments and your engineering shapes how the world's leading legal professionals work. With one million professionals now relying on CoCounsel across 107 countries, the work you do here will matter.

As a Senior Software Engineer – AI, you will work side-by-side with the AI Solutions Architect, Senior Legal AI Engineers, and fellow AI engineers to design, build, and deploy bespoke AI solutions from zero to one for law firms and corporate legal departments. You are a strong, independent engineer who takes solution designs and architectural direction and drives them all the way to production — owning your work end to end, contributing to solution design, and bringing deep AI engineering craft to every engagement.

This is not a back-office engineering role. You will participate in technical discovery with clients, contribute to solution design, develop tailored AI systems, and own the quality of what you ship — ensuring the AI we deploy consistently meets the high trust standards of legal professionals. Your architecture will be reviewed and shaped in partnership with the AI Solutions Architect, giving you a strong technical support structure while you focus on building.

Key Responsibilities

Solution Design & Engineering

• •Design and implement AI-powered solutions tailored to law firm and corporate legal workflows, including contract review, litigation support, discovery automation, and regulatory analysis.
• •Work within architectural frameworks set by the AI Solutions Architect to build scalable, secure, and maintainable AI systems.
• •Architect RAG pipelines, custom AI agents, and multi-component systems that integrate securely into client environments, including document management systems and enterprise platforms.
• •Build and configure multi-step agentic workflows leveraging CoCounsel, Westlaw, and Practical Law capabilities, as well as third-party AI APIs and enterprise integrations.
• •Implement Agent-to-Agent (A2A) workflows and Model Context Protocol (MCP) integrations to enable interoperability between AI agents and external tools, data sources, and enterprise systems.
• •Design and deploy bespoke automation pipelines and integration scripts tailored to each customer's use case.
• •Implement MLOps and LLMOps practices to support scalable, maintainable deployment of AI solutions in production.

Observability, Quality & Trust

• •Implement monitoring, logging, and tracing solutions to track LLM performance, latency, token usage, and system health in production.
• •Leverage AI evaluation platforms (such as LangSmith, Braintrust, or Weights & Biases) to assess model outputs, track evaluation metrics over time, and surface quality regressions across deployed solutions.
• •Contribute to LLM evaluation efforts — assessing model outputs for accuracy, citation quality, hallucination risk, and fitness for high-stakes legal use — in partnership with the AI Solutions Architect and product team.
• •Develop and maintain evaluation benchmarks, test suites, and red-teaming protocols grounded in real legal workflows, and translate findings into structured input for product and engineering roadmaps.
• •Ensure all solutions comply with ethical AI principles, data protection standards, and enterprise security requirements.

Discovery & Client Engagement

• •Participate in technical discovery on client engagements — contributing to use case scoping, solution design discussions, and early-stage prototyping.
• •Translate complex legal workflows, pain points, and client goals into concrete engineering tasks and implementation plans.
• •Conduct technical demonstrations and early-stage prototyping to support solution design and client confidence.
• •Build trust with legal professionals through fluency in legal terminology, workflows, and the real-world constraints of legal practice.
• •Work directly with legal SMEs to understand their professional reasoning and judgement, and translate that domain knowledge into AI pipeline design, evaluation criteria, and benchmarks.

Technical Contribution & Collaboration

• •Collaborate closely with Senior Legal AI Engineers, the AI Solutions Architect, and fellow AI engineers to deliver high-quality solutions across engagements.
• •Contribute to team knowledge-sharing, internal documentation, and reusable solution templates that accelerate delivery across the team.
• •Evaluate and prototype cutting-edge AI techniques, including emerging agentic frameworks, MCP-based tool integrations, and prompt engineering approaches, to improve output quality across diverse legal use cases.
• •Stay current with AI research and the rapidly evolving legal AI landscape, bringing new ideas and approaches into the team.

Required Qualifications

• •5+ years of software engineering experience with a strong foundation in system design, distributed systems, and clean architecture.
• •At least 2 years of hands-on experience building and deploying AI/ML solutions in production, including LLM-powered applications.
• •Proficiency in orchestration frameworks such as LangChain, LangGraph, PydanticAI, or AutoGen, and solid understanding of RAG architectures and vector databases.
• •Experience with Model Context Protocol (MCP) and Agent-to-Agent (A2A) workflow patterns for building interoperable, tool-augmented AI systems.
• •Strong proficiency in Python (FastAPI or equivalent) and experience building RESTful and async, event-driven architectures.
• •Experience with cloud platforms (AWS, Azure, or GCP) and familiarity with Infrastructure as Code tools (Terraform, CDK, or equivalent).
• •Experience with observability and monitoring stacks (Datadog, Grafana, Prometheus, or similar) for production AI systems.
• •Experience with AI evaluation platforms (e.g., LangSmith, Langfuse, Braintrust, Weights & Biases) and the ability to contribute to structured evaluation pipelines for LLM-powered applications.
• •Strong background in secure API development, including authentication and authorization protocols (OAuth 2.0, OpenID Connect, JWT).
• •Strong prompt engineering skills — ability to design, iterate, and optimize LLM prompts for specialized domain tasks.
• •Demonstrated fluency with AI-assisted development tools (e.g., Claude Code, Cursor, or GitHub Copilot) — this teams operates AI-natively across all engineering workflows, not just client deliverables.
• •Excellent communication skills with the ability to engage credibly with both engineers and non-technical stakeholders, including legal professionals.
• •Comfort working independently, navigating ambiguity, and contributing meaningfully in client-facing workshops and discovery sessions.

Preferred Qualifications

• •Fluency in legal terminology and demonstrated experience working directly within or alongside legal teams.
• •Experience in a regulated industry — legal, fintech, or healthcare — and familiarity with associated compliance and data privacy requirements.
• •Experience with legal platforms, document management systems, and legal technology ecosystems.
• •Experience with container orchestration (Docker/Kubernetes).
• •Knowledge of MLOps and LLMOps practices for production AI system lifecycle management.
• •Understanding of the scaling, limitations, and fine-tuning of transformer-based models.
• •Familiarity with emerging AI interoperability standards and protocols, including MCP ecosystem tooling and A2A communication patterns.
• •A portfolio of projects demonstrating in skill building complex, production-grade AI solutions.

What You'll Be Part Of

Over 20,000 law firms and corporate legal