• Design, implement, and iterate on LangGraph workflows that support structured requirement capture, clarification loops, stateful routing, and tool orchestration. • Build reusable workflow components (nodes, validators, schema-enforced outputs, guardrails) and standard patterns for retries, fallbacks, and human-in-the-loop review where appropriate. • Implement robust tool execution patterns (timeouts, error taxonomy, idempotency where needed) and ensure safe, deterministic structured outputs for downstream automation. • Partner with Business Analysts and org leaders during discovery and refinement to clarify goals, constraints, and success metrics; translate requirements into sprint-ready tickets and acceptance criteria. • Build and maintain agent-callable services/APIs for Snowflake retrieval, operational actions (e.g., Slack/workflows), and data enrichment/packaging. • Establish evaluation and observability for agent workflows (golden cases, regression suites, tool mocks; latency/cost/error monitoring) and contribute to pragmatic SLOs. • Contribute to our in-house transcription application through agent adjustments and infrastructure improvements, including transcript ingestion, normalization (timestamps/speaker turns/metadata), storage, and retrieval surfaces for downstream tooling. • Improve reliability of transcription pipelines through queue/worker architectures, retries/backoff, idempotency, and replay tooling. • Implement and iterate on rubric-based call scoring (versioned rubrics, explainable outputs, calibration support) and publish scoring artifacts that are usable for coaching and operational workflows. • Implement streaming considerations where applicable (session handling, backpressure/reconnect patterns) and ensure clean handoffs between capture, transcription, and scoring stages. • Deliver scoped components and workflows with guidance on architecture and design decisions from senior engineers or tech leads.

• Own the design and implementation of backend systems and APIs using JavaScript/TypeScript • Build and ship full-stack features using Next.js, with an emphasis on performance and reliability • Design, evolve, and optimize PostgreSQL schemas and queries at scale • Make architectural decisions with long-term scalability in mind • Deploy and operate services on AWS (EC2, RDS, S3, Lambda, etc.) • Monitor, debug, and resolve production issues • Collaborate closely with product and design to turn ideas into shipped features • Review code, set standards, and mentor other engineers • Improve development workflows, tooling, and system observability

• Design, develop, and deploy complex, multi-step AI agents with tool-use, memory and context management, leveraging LangChain, LangGraph, and LangSmith frameworks, and integrating with foundational Large Language Models (LLMs). • Integrate LLMs with external tools, APIs, knowledge sources, and enterprise systems. • Build retrieval layers (e.g., hybrid search, vector search, metadata filtering) optimized for LLM consumption. • Stay up to date with cutting-edge technologies and best practices for building Gen AI-based applications.

• Collaborate with Machine Learning Engineers to build the ML training pipelines that process massive 3D datasets, orchestrate model training, and enable continuous model improvements. • Streamline the ML lifecycle, from data labeling and experimentation to deployment, by optimizing internal ML components and reducing technical debt. • Develop and maintain cloud-native systems and tooling (GCP/Kubernetes) that support Dandy’s 3D dental products in a secure, well-tested, and high-performing manner. • Write clean, maintainable code and tests that set the standard for our internal best practices. • Partner with stakeholders across the Engineering organization to influence long-term architectural goals and maintain a high-quality bar.