Lead AI Solutions Architect - Legal Operations

Mindlance
New York, NY, United States
2 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Amazon Web Services Computer Vision Microsoft Azure Cloud Computing Data Governance Machine Learning Automation of Marketing Natural Language Processing Microsoft Copilot Google Cloud Cloud Platform System
+5 more
Large Language Models Agentic-AI Build Tools Machine Learning Operations GPT

Requirements

Key Responsibilities Remote 37.5 Work Week Knows How To Build Systems With Different Ai Tools (Chatgpt, Copilot) Requesting Br For Level 2 (5-9 Years) & Level 3 (10 Years) Potential Conversion After 6 Months Based On Performance Legal Experience Is Preferred But Not Required. Heavy Focus On Tech Ai & Technology ArchitectureArchitect how AI agents, large language models, and automation platforms integrate across our legal workflowsDirectly build internal tools, working side-by-side with attorneys to understand requirements, prototype solutions, iterate based on feedback, and ship production-quality applicationsOwn the full engineering lifecycle of internal tools: design, development, testing, deployment, and ongoing maintenanceEnsure tools comply with data governance, security, and confidentiality requirementsMonitor tool performance and usage, proactively identifying where systems need to be rethought or rebuiltTranslate legal requirements into engineering specifications and build solutions that attorneys find intuitive and trustCollaborate with IT and Security to ensure tools are properly hosted, secured, and integrated with existing infrastructureLegal Process EngineeringMap current legal workflows end-to-end to identify inefficiencies, manual bottlenecks, and automation opportunitiesRedesign processes before deploying technology, fixing the process first, then building the system around itDevelop and maintain a living operating model for how legal services are designed, staffed, and deliveredDefine what stays in-house, what AI handles, and what goes to outside counsel, and build the workflows to execute that modelRequirementsAdvanced 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 monitoringStrategic thinking: Ability to develop a long-term technical vision and align it with overall product and business strategyExcellent 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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