Senior Software Engineer - AI, CoCounsel FDE

Thomson Reuters•Washington, DC

1d•$118,400 - $219,800•Hybrid

About The Position

The Senior Software Engineer – AI will design, build, and deploy bespoke AI solutions for law firms and corporate legal departments as part of Thomson Reuters’ CoCounsel Forward Deployed Engineering team. Working with the AI Solutions Architect, Senior Legal AI Engineers, and other AI engineers, the engineer will take architectural direction and solution designs through to production, contribute to solution design, and own the quality and delivery of their work end to end. This is a client-facing engineering role involving technical discovery, use-case scoping, prototyping, and collaboration with legal subject-matter experts. The engineer will develop tailored AI systems for legal workflows, including contract review, litigation support, discovery automation, and regulatory analysis. Solutions must be scalable, secure, maintainable, and suitable for high-trust legal use. The role also contributes to evaluation, observability, reusable engineering practices, and the team’s approach to emerging AI techniques and standards.

Requirements

At least 5 years of software engineering experience, including 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 with orchestration frameworks such as LangChain, LangGraph, PydanticAI, or AutoGen, and a solid understanding of RAG architectures and vector databases.
Experience with MCP and A2A workflow patterns for interoperable, tool-augmented AI systems.
Strong proficiency in Python, including FastAPI or an equivalent framework, and experience building RESTful and asynchronous, event-driven architectures.
Experience with AWS, Azure, or GCP and familiarity with Infrastructure as Code tools such as Terraform or CDK.
Experience with observability and monitoring tools such as Datadog, Grafana, or Prometheus for production AI systems.
Experience with AI evaluation platforms such as LangSmith, Langfuse, Braintrust, or Weights & Biases, and ability to contribute to structured evaluation pipelines for LLM applications.
Strong background in secure API development, including OAuth 2.0, OpenID Connect, and JWT authentication and authorization protocols.
Strong prompt-engineering skills, including designing, iterating, and optimizing prompts for specialized domain tasks.
Demonstrated fluency with AI-assisted development tools such as Claude Code, Cursor, or GitHub Copilot.
Excellent communication skills and ability to engage credibly with engineers, non-technical stakeholders, and legal professionals.
Ability to work independently, navigate ambiguity, and contribute meaningfully in client-facing workshops and discovery sessions.

Nice To Haves

Fluency in legal terminology and experience working directly within or alongside legal teams.
Experience in a regulated industry such as 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, including Docker or 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 demonstrating skill in building complex, production-grade AI solutions.

Responsibilities

Design and implement AI-powered solutions for law firm and corporate legal workflows, including contract review, litigation support, discovery automation, and regulatory analysis.
Build scalable, secure, maintainable AI systems within architectural frameworks established with the AI Solutions Architect.
Architect retrieval-augmented generation (RAG) pipelines, custom AI agents, and multi-component systems that integrate with client environments, document management systems, and enterprise platforms.
Build and configure multi-step agentic workflows using CoCounsel, Westlaw, Practical Law, third-party AI APIs, and enterprise integrations.
Implement Agent-to-Agent (A2A) workflows and Model Context Protocol (MCP) integrations for interoperability with agents, tools, data sources, and enterprise systems.
Design and deploy customer-specific automation pipelines and integration scripts.
Implement MLOps and LLMOps practices for scalable, maintainable production deployments.
Implement production monitoring, logging, and tracing for LLM performance, latency, token usage, and system health.
Use AI evaluation platforms to assess model outputs, track evaluation metrics, and identify quality regressions.
Contribute to LLM evaluation for accuracy, citation quality, hallucination risk, and suitability for high-stakes legal use.
Develop and maintain evaluation benchmarks, test suites, and red-teaming protocols based on real legal workflows; communicate findings to product and engineering roadmaps.
Ensure solutions comply with ethical AI principles, data protection standards, and enterprise security requirements.
Participate in client technical discovery, use-case scoping, solution design discussions, and early-stage prototyping.
Translate legal workflows, client pain points, and goals into engineering tasks and implementation plans.
Conduct technical demonstrations and prototypes to support solution design and client confidence.
Build trust with legal professionals through understanding of legal terminology, workflows, and practical constraints.
Work with legal subject-matter experts to translate professional reasoning and judgment into AI pipeline design, evaluation criteria, and benchmarks.
Collaborate with Senior Legal AI Engineers, the AI Solutions Architect, and other AI engineers across engagements.
Contribute to knowledge-sharing, internal documentation, and reusable solution templates.
Evaluate and prototype emerging AI techniques, agentic frameworks, MCP tool integrations, and prompt-engineering approaches.
Keep current with AI research and the legal AI landscape and bring relevant approaches to the team.

Benefits

Flexible vacation and paid time off, including sick and safe time and paid holidays.
Two company-wide mental health days off annually.
Access to the Headspace app and Employee Assistance Program.
Retirement savings plan with company match.
Tuition reimbursement.
Employee incentive programs and possible annual bonus based on enterprise and individual performance.
Health, dental, vision, disability, and life insurance.
Parental leave and sabbatical leave.
Flexible work arrangements, including work from anywhere for up to 8 weeks per year.
Two paid volunteer days off annually and opportunities for pro-bono consulting and ESG initiatives.
Flexible Spending and Health Savings Accounts.
Fitness reimbursement.
Optional hospital, accident and sickness insurance; optional life and AD&D insurance.
Group Legal Identity Theft Protection benefit.
Access to a 529 Plan.
Commuter benefits.
Adoption and surrogacy assistance.
Employee Stock Purchase Plan.
Resources supporting mental, physical, and financial wellbeing.