• Design, train, and evaluate ML models for text and voice analysis • Own feature engineering, labeling strategies, and evaluation metrics • Build and evolve ML pipelines that support experimentation and continuous improvement • Collaborate with data engineering teams on ETL, data quality, and data versioning • Influence modeling approaches and technical direction across the product

• Build and ship production features across the frontend and backend • Develop operator dashboard UI components (Next.js, React, TypeScript) • Design and implement API endpoints and backend logic (Bun/Elysia, PostgreSQL) • Integrate with external enterprise APIs and internal AI services • Write and maintain tests for critical workflows • Own and evolve key product areas, including the approval queue • Build and refine SLA tracking, escalation logic, and operator workflows • Create analytics views for call patterns, approvals, and performance metrics • Support live production operations during the enterprise pilot • Monitor, debug, and resolve issues in a real-time AI system • Respond to operator and stakeholder feedback • Improve system reliability, observability, and performance • Work closely with senior engineers on architecture and technical decisions • Participate in code reviews (giving and receiving feedback) • Document systems and features to support long-term maintainability

• A growing, technology-driven professional services organization is seeking an AI Architect to join its team. • This role is a senior technical and strategic leadership position responsible for driving the organization’s AI vision from concept through commercialization. • The AI Architect will design and implement AI systems, lead development teams, guide enterprise AI architecture, and partner closely with executive leadership and business stakeholders to identify opportunities, drive adoption, and deliver measurable business value. • This role blends deep hands-on technical expertise with product strategy, business alignment, and organizational leadership. • Experience deploying AI solutions at scale in real-world production environments is essential. Experience in legal services, litigation support, medical records, insurance, or other regulated industries is a strong plus.

• Exploratory analyses and data visualization to understand the business and to apply LLM-based solutions; • Development of AI applications using vector databases, LLMs, single-agent and multi-agent architectures; • Development of validation pipelines for LLM applications, including datasets and LLM-as-a-judge approaches; • Development of data pipelines; • Development of small back-end and front-end components to build proofs of concept for LLM applications; • Reading technical blogs, conference talks and research papers to generate hypotheses for improving LLM applications.