Lead AI Solutions Architect is responsible for designing and implementing AI solutions for legal operations. The role involves building internal tools, integrating AI agents, and automating legal workflows. The ideal candidate should have hands-on technical expertise in AI/ML engineering and modern full-stack development.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Job Description:
Job Title: Lead AI Solutions Architect – Legal Operations
Location : 100% Remote (New York, NY – Only EST candidates preferred)
Type: Contract
Client: Direct Client
MUST HAVE:
- Knows how to build systems with different AI tools (ChatGPT, Copilot)
- Legal experience is preferred but not required.
- Heavy focus on tech.
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Job Description:
AI & Technology Architecture
- Architect how AI agents, large language models, and automation platforms integrate across our legal workflows
- Directly build internal tools, working side-by-side with attorneys to understand requirements, prototype solutions, iterate based on feedback, and ship production-quality applications
- Own the full engineering lifecycle of internal tools: design, development, testing, deployment, and ongoing maintenance
- Ensure tools comply with data governance, security, and confidentiality requirements
- Monitor tool performance and usage, proactively identifying where systems need to be rethought or rebuilt
- Translate legal requirements into engineering specifications and build solutions that attorneys find intuitive and trust
- Collaborate with IT and Security to ensure tools are properly hosted, secured, and integrated with existing infrastructure
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Legal Process Engineering
- Map current legal workflows end-to-end to identify inefficiencies, manual bottlenecks, and automation opportunities
- Redesign processes before deploying technology, fixing the process first, then building the system around it
- Develop and maintain a living operating model for how legal services are designed, staffed, and delivered
- Define what stays in-house, what AI handles, and what goes to outside counsel, and build the workflows to execute that model
Requirements
- Advanced hands-on technical expertise: Demonstrated hands-on expertise in both AI/ML engineering (including LLMs, computer vision, NLP, etc.) and modern full-stack development as an individual contributor.
- Experience with cloud and MLOps: Proficiency with cloud environments (AWS, GCP, Azure) and scaling MLOps for model training, deployment, inference, and monitoring
- Strategic thinking: Ability to develop a long-term technical vision and align it with overall product and business strategy
- Excellent internal and external communication: The ability to communicate complex technical concepts to both technical and non-technical audiences, including executive leadership and partners
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