Senior AI Full Stack Engineer

Built AI-powered document generation capabilities that enabled attorneys to automate creation of wills, trusts, powers of attorney, and estate planning documents, reducing document preparation effort by more than 70%. Developed enterprise knowledge retrieval and semantic search solutions that enabled attorneys to access decades of legal expertise, templates, and institutional knowledge through natural language search, improving drafting efficiency and document accuracy. Built conversational AI capabilities allowing legal professionals to query estate documents, client records, and historical case information using natural language, reducing manual research effort and improving productivity. Developed AI-powered voice agents using Retell AI that automated lead qualification, client intake, appointment scheduling, and follow-up workflows, improving customer engagement while reducing manual effort for sales and operations teams. Developed agentic AI workflows that automated multi-step trust funding and estate administration processes, streamlining beneficiary updates, account retitling, deed preparation, and related legal workflows. Designed human-in-the-loop AI workflows that combined AI-generated outputs with attorney review and approval processes, accelerating document creation while maintaining legal quality, accuracy, and compliance standards. Built production-grade RAG infrastructure supporting document ingestion, chunking, embedding generation, vector indexing, retrieval optimization, and continuous knowledge synchronization across thousands of legal documents. Developed automated knowledge ingestion and synchronization pipelines that continuously processed new and updated legal content, ensuring retrieval systems remained current and aligned with evolving legal documentation. Implemented scheduled embedding refresh and vector indexing workflows to maintain retrieval quality and knowledge coverage as legal content repositories expanded. Improved retrieval quality through embedding experimentation, retrieval tuning, chunking optimization, reranking strategies, and evaluation frameworks, increasing answer relevance while reducing hallucinated responses. Designed prompt engineering and AI evaluation frameworks that established quality benchmarks, automated testing processes, and feedback loops to improve consistency, reliability, and legal accuracy of AI-generated outputs. Built automated evaluation and regression testing pipelines to validate retrieval relevance, answer quality, citation accuracy, and document generation performance before production releases. Implemented guardrails, validation mechanisms, and compliance controls to ensure generated legal content met business, regulatory, and quality requirements while minimizing unsupported AI responses. Established safe deployment processes for model, prompt, retrieval, and embedding upgrades using feature flags, canary releases, shadow testing, staged rollouts, and rollback strategies, reducing production risk and maintaining service reliability. Built comprehensive observability across AI workflows, monitoring retrieval effectiveness, response quality, latency, token consumption, operational costs, and user feedback to drive continuous platform improvements. Optimized AI operating costs through model selection strategies, intelligent provider routing, prompt optimization, embedding reuse, caching mechanisms, and retrieval efficiency improvements while maintaining quality and availability targets. Designed scalable AI infrastructure supporting high-volume document generation, retrieval, and conversational AI workloads, ensuring reliability, security, and cost efficiency as platform adoption increased. Modernized the attorney-facing platform using Next.js, FastAPI, and AWS cloud services, improving application performance, scalability, reliability, and overall user experience. Implemented enterprise-grade security, auditability, encryption, and role-based access controls supporting SOC 2 compliance and secure handling of sensitive legal and financial information. Established CI/CD pipelines, automated testing frameworks, observability tooling, and deployment processes that accelerated release cycles, improved engineering productivity, and reduced production incidents. Partnered closely with attorneys, trust companies, and product stakeholders to transform complex legal workflows into AI-powered solutions that improved operational efficiency, customer adoption, and business outcomes.

Built intelligent coach-matching capabilities that connected employees and leaders with the most relevant coaches based on professional goals, leadership challenges, and development needs, improving coaching engagement and program effectiveness. Developed AI-powered coaching assistants that generated session summaries, key insights, action items, and personalized recommendations, reducing administrative effort for coaches while improving accountability and follow-through for participants. Built personalized coaching preparation workflows that leveraged AI and historical coaching context to generate meeting agendas, discussion topics, and development recommendations, improving coaching effectiveness and participant engagement. Developed leadership assessment, pulse survey, and feedback management solutions that enabled organizations to continuously measure employee development, leadership effectiveness, and coaching outcomes rather than relying solely on periodic reviews. Built enterprise analytics and reporting platforms that provided HR and People teams with visibility into coaching engagement, leadership development, employee growth trends, and program ROI across large organizations. Developed AI-driven recommendation capabilities that personalized coaching journeys, learning paths, and development plans based on employee goals, coaching history, behavioral assessments, and engagement patterns. Designed scalable data pipelines and analytics solutions supporting workforce insights, coaching performance measurement, employee segmentation, and executive reporting initiatives. Built enterprise feedback and 360-degree assessment workflows that enabled managers, peers, and direct reports to provide structured developmental feedback, supporting leadership growth across hundreds of organizations. Developed coach onboarding, credential management, and profile administration workflows that streamlined enrollment processes and reduced operational overhead for coaching program administrators. Built real-time collaboration and notification capabilities supporting coaching sessions, participant engagement, scheduling workflows, and asynchronous communication experiences. Implemented AI evaluation and experimentation frameworks to assess recommendation quality, personalization effectiveness, and coaching outcome improvements across multiple product features. Designed secure and compliant data management solutions supporting GDPR requirements, access controls, data retention policies, and enterprise customer governance standards. Established CI/CD pipelines, automated testing frameworks, observability tooling, and deployment processes that improved release quality, accelerated feature delivery, and reduced production issues. Partnered closely with executive coaches, HR leaders, People Operations teams, and enterprise customers to translate workforce development challenges into scalable AI-powered product capabilities. Contributed to the evolution of Mento's AI strategy by evaluating machine learning and generative AI approaches for coaching recommendations, personalization, behavioral insights, and workforce development initiatives.