• Partner with the AI Engineering lead to ship features from day one, ramping quickly into independent contributions on the Elixir platform • Complete a Forward Deployed Engineer (FDE) rotation in months 1–2 — embed with a business function to learn what the platform must support by being the one who needs it • Contribute to Elixir’s core platform: skill execution runtime, tool/MCP registry, retrieval and memory primitives, evaluation harnesses, observability and cost telemetry, and multi-model routing • Extend the skills surface: creating, editing, version control, AI assistance, testing, benchmarking, and permissions • Build event-driven background agents — absorb support-ticket pre-triage, ambient agents, and webhook-driven flows into Elixir as native event-driven flows • Productize what FDEs build — extract platform-level needs so each engagement compounds rather than serializes • Make Elixir secure for production — PII handling, access control, audit logging, evaluation gates, sandboxing, and multi-tenant isolation • Maintain clear and proactive communication with stakeholders and cross-functional partners • Demonstrate self-sufficiency by independently managing tasks, problem-solving, and meeting deadlines while adapting to new challenges.

• Build and ship LLM-powered features across the DerivativeEDGE platform, working directly with domain experts to translate complex financial workflows into reliable AI capabilities. • Design and implement agentic workflows that can reason, act, and recover gracefully across multi-step processes in a production environment. • Own the data engineering layer that feeds AI systems: pipelines, retrieval architectures, context design, and data quality. • Move fluidly between experimentation and production. You will prototype quickly, evaluate honestly, and know when something is ready to ship. • Contribute to the AI Lab's broader technical direction, including evaluations, tooling, MLOps practices, and the patterns the team builds on. • Depending on where projects take you, work may also touch NLP, model fine-tuning, synthetic data, or reinforcement learning.

• Design, develop, and deploy AI-powered applications, workflows, and automation solutions using LLMs and related AI technologies. • Build and maintain integrations between HubSpot, CRM platforms, EHR systems, business applications, and third-party tools using APIs and middleware solutions. • Develop, test, and optimize prompts, workflows, and AI agents to improve response accuracy, efficiency, and user experience. • Configure and optimize HubSpot Marketing, Finance, Sales, Service, and CMS hubs, including workflows, reporting, data management, and automation. • Collaborate with marketing, finance, sales, customer service, and product teams to identify automation opportunities and translate requirements into scalable technical solutions. • Create dashboards, reports, and data models to monitor performance, measure KPIs, and generate actionable insights. • Conduct end-to-end testing, troubleshooting, and performance monitoring of AI and integration solutions. • Maintain data quality, governance, security, and documentation across integrated systems. • Evaluate emerging AI technologies, integration tools, and automation best practices, recommending improvements where appropriate. • Provide technical guidance and training to internal teams on AI, automation, and HubSpot capabilities.

• Design the solution's multi-agent architecture, defining agent topology, orchestration strategies, intent routing, and state management across the customer journey • Create and implement specialized agents, defining roles, tools, memory, context and handoff rules between agents at each step of the developed flow • Develop and optimize prompts, policies and guardrails, ensuring consistent, safe behavior aligned with internal guidelines (protection against prompt injection, jailbreak and leakage of sensitive information) • Define and operate evaluation metrics, creating automated testing pipelines and continuously monitoring performance, latency, cost per interaction and response quality • Ensure data security and privacy by implementing access controls, anonymization and interaction traceability • Manage the lifecycle of models and agents in production (versioning, controlled rollouts, drift monitoring and fallback strategies) • Create and maintain technical documentation and engineering standards (agent contracts, prompt templates, operational runbooks and development guidelines) • Collaborate with product, data and software engineering teams to ensure reliable integration of agents with other systems.