Role Description Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. While each project involves unique tasks, contributors may: - Generate prompts that challenge AI; - Evaluate AI-generated solutions for correctness, assumptions, and logic; - Improve AI reasoning to align with first principles and accepted standards; - Apply structured scoring criteria to assess multi-step problem solving. Qualifications - Degree in law (Bachelor, J.D., LLM) within the EU & UK context; - 2+ years of legal practice experience within EU & UK jurisdiction; - Strong written English (C1+). Requirements This opportunity is a good fit for legal consultants, attorneys with experience in EU & UK law open to part-time, non-permanent projects. Benefits - Earn up to $75 per hour equivalent, depending on level and pace of contribution; - Compensation varies across projects depending on scope, complexity, and required expertise. Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation Please note that other projects on the platform may offer different earning levels based on their requirements.

Role Description Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves: - Generate prompts that challenge AI; - Evaluate AI-generated solutions for correctness, assumptions, and logic; - Improve AI reasoning to align with first principles and accepted standards; - Apply structured scoring criteria to assess multi-step problem solving. Qualifications - Degree in law (Bachelor, J.D., LLM, FLLM) within the US context; - 2+ years of legal practice experience within US jurisdiction; - Strong written English (C1/C2); - Stable internet connection. Requirements This opportunity is a good fit for legal consultants, attorneys with experience in US law open to part-time, non-permanent projects. Benefits - Compensation up to $75 per hour equivalent, depending on level and pace of contribution; - Compensation varies across projects depending on scope, complexity, and required expertise; - Other projects on the platform may offer different earning levels based on their requirements. Company Description Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid. Project time expectations: - Tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements; - This is an estimate, not a guaranteed workload, and applies only while the project is active.

• Own the architecture of EIS's agentic platform: agent orchestration, MCP-native tool ecosystems, agent memory (short-term, long-term, semantic), planning, and tool/function calling patterns reusable across product domains. • Enable and provide support for domain teams for vertical insurance agents and the horizontal capabilities (RAG, retrieval, instructional flows) they compose from. • Define and enforce levels of autonomy — assistive, semi-autonomous, autonomous — with explicit human-in-the-loop checkpoints, escalation paths, and reversibility for high-stakes actions in regulated workflows. • Drive the MCP strategy: which capabilities EIS exposes as MCP servers to internal and partner agents, how our agents consume external MCP tools, and the tool registry, schemas, and versioning that keep this scalable. • Maintain the multiple stack approach as a first-class capability: Typescript, and Java. Help teams to pick the right stack per agent and keep all aligned through shared configuration artefacts, prompt management, and evaluation tooling. • Lead Architecture Decision Records (ADRs) for agentic capabilities; partner with Platform, Security/InfoSec, and DevOps so agents are observable, testable, sandboxed, and compliant by default. • Drive AI DevOps for agents: trace capture and replay, eval harnesses (task success, tool-use correctness, regression), prompt and model versioning, cost and latency budgets per agent, and progressive rollout strategies. • Set safe-AI standards for agentic systems: prompt injection and tool-poisoning defenses, action allow-lists, blast-radius controls, PII handling, data residency, and bias mitigation. Treat agent safety as a first-class architectural concern. • Translate insurance use cases into production agent designs with product strategists and domain architects; provide technical leadership and mentorship; communicate agentic trade-offs (autonomy, reliability, cost, safety) clearly to executives, customers, and engineers.

- Own the architecture of EIS's agentic platform: agent orchestration, MCP-native tool ecosystems, agent memory (short-term, long-term, semantic), planning, and tool/function calling patterns reusable across product domains. - Enable and provide support for domain teams for vertical insurance agents and the horizontal capabilities (RAG, retrieval, instructional flows) they compose from. - Define and enforce levels of autonomy — assistive, semi-autonomous, autonomous — with explicit human-in-the-loop checkpoints, escalation paths, and reversibility for high-stakes actions in regulated workflows. - Drive the MCP strategy: which capabilities EIS exposes as MCP servers to internal and partner agents, how our agents consume external MCP tools, and the tool registry, schemas, and versioning that keep this scalable. - Maintain the multiple stack approach as a first-class capability: Typescript, and Java. Help teams to pick the right stack per agent and keep all aligned through shared configuration artefacts, prompt management, and evaluation tooling. - Lead Architecture Decision Records (ADRs) for agentic capabilities; partner with Platform, Security/InfoSec, and DevOps so agents are observable, testable, sandboxed, and compliant by default. - Drive AI DevOps for agents: trace capture and replay, eval harnesses (task success, tool-use correctness, regression), prompt and model versioning, cost and latency budgets per agent, and progressive rollout strategies. - Set safe-AI standards for agentic systems: prompt injection and tool-poisoning defenses, action allow-lists, blast-radius controls, PII handling, data residency, and bias mitigation. Treat agent safety as a first-class architectural concern. - Translate insurance use cases into production agent designs with product strategists and domain architects; provide technical leadership and mentorship; communicate agentic trade-offs (autonomy, reliability, cost, safety) clearly to executives, customers, and engineers.