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This role sits at the intersection of AI engineering, data scientist, developer enablement, and customer engagement. You will partner with Product, Engineering, Applied Science, and AI Platform teams to support implementation decisions, accelerate AI adoption, and help teams adopt reusable AI engineering patterns and implementation best practices. This is a deeply hands-on role focused on building, prototyping, and iterating on AI-powered experiences.
Job Responsibility
Spend real time with lawyers, legal operations teams, and our internal subject-matter experts
Translate ambiguous, half-formed customer pain into crisp problem statements the team can build against
Collaborate closely with customers and internal stakeholders to prototype, validate, and refine AI-powered workflows and user experiences
Bring the customer voice back into our roadmaps, our model choices, and our trade-offs
Contribute to AI-powered applications and workflows for legal and business use cases
Implement and iterate on LLM application capabilities such as prompt engineering, multi-step workflows, tool calling, and lightweight agent patterns
Contribute to scalable orchestration layers for prompting, retrieval, and tool integration across AI services
Work with frameworks such as LangChain, LangGraph, LlamaIndex, MCP/A2A, OpenAI SDKs, Google ADK, and/or Anthropic/Claude APIs to prototype and productionize AI capabilities
Participate in experimentation, testing, and performance optimization activities for LLM-based applications in production environments
Support adoption of AI engineering practices by helping software engineering teams incrementally integrate machine learning and generative AI capabilities into existing products and workflows
Promote reusable AI/ML engineering standards, tooling, and best practices that reduce friction for teams adopting AI and machine learning technologies
Help software engineers expand their capabilities in ML-oriented development for applicable use cases without requiring deep data science specialization
Requirements
6+ years of experience as a Software Engineer, AI Engineer, Platform Engineer, or related technical role
Strong production experience building LLM-powered applications and deployment at scale
Strong programming skills in Python and experience building scalable production services and APIs
Experience designing and implementing AI application architectures in cloud-native environments
Hands-on experience with modern AI engineering frameworks and tooling such as LangChain, LangGraph, LlamaIndex, OpenAI APIs, Anthropic APIs, MCP, or equivalent systems
Experience building AI workflows involving retrieval, tool calling, orchestration, context management, and structured generation
Familiarity with AI observability, evaluation frameworks, and production monitoring
Experience deploying and operating AI systems on AWS, Azure, or GCP
Comfortable working in evolving environments and collaborating across teams to deliver AI-powered features and workflows
Strong communication and collaboration skills with the ability to work effectively across engineering, product, and business teams
Experience contributing to production systems and collaborating on practical implementation trade-offs
Nice to have
Experience in legal technology, enterprise SaaS, compliance, financial services, healthcare, or other regulated industries
Experience building AI copilots, AI assistants, workflow automation systems, or multi-agent platforms
Familiarity with developer platforms, SDK development, API productization, or AI platform engineering
Experience facilitating technical workshops, hackathons, or developer enablement initiatives
Strong understanding of AI UX and conversational workflow system design
Experience with AI evaluation, guardrails, policy enforcement, and responsible AI deployment
Familiarity with inference optimization, LLM serving infrastructure, or AI infrastructure tooling
Full-stack or frontend engineering experience for rapid prototyping and developer experience optimization
Open-source contributions, technical blogging, conference speaking, or AI engineering community involvement