• You’ll build and maintain reliable automation that interacts with real-world web portals and third-party systems—where correctness, traceability, and security matter as much as speed. • Own the design, development, and operation of web-portal automation workflows using Playwright (and related tooling). • Build automation services primarily in Python, with strong supporting development in TypeScript. • Implement evaluation/validation (“evals”) to measure automation accuracy, catch regressions, and improve reliability over time. • Maintain strong audit-trail discipline: deterministic logging, event traces, artifacts, and reproducible runs for debugging and compliance needs. • Design automation with a security-first mindset, following best practices for handling sensitive workflows and data. • Collaborate with engineering/product stakeholders to translate ambiguous real-world processes into pragmatic, shippable automation. • Improve robustness against common automation challenges (dynamic UIs, flaky states, rate limits, auth flows, partial failures) while keeping systems maintainable.

• Set architectural direction for products emerging from Labs, from prototype to production • Design and build production-grade agentic workflows: retrieval, tool use, multi-step planning, human-in-the-loop checkpoints, and safety guardrails • Be the senior technical voice in architectural decisions that span multiple teams • Establish technical standards and engineering practices that extend across the organization • Make significant individual contributions to the SaaS product across Azure and AWS • Collaborate with product, design, and engineering across the full SDLC • Mentor and guide engineers and actively influence engineering culture • Ensure performance, quality, and responsiveness of customer-facing applications and the agentic systems built on top of them • Implement and maintain modern automated testing practices, including evals for AI components • Contribute to modernization and consolidation of legacy surfaces as integration touch points emerge

• Co-create solutions with business and technical stakeholders through workshops, rapid iterations, and hands-on delivery. • Locate, qualify, and secure access to the data required for each use case, working directly with the Data Engineer. • Translate use cases into production-ready GenAI and agentic AI solutions, including RAG architectures, intelligent assistants, and AI-enabled workflows. • Prototype, test, deploy, monitor, and improve solutions in real client environments using feedback from users and domain experts. • Work with Data Engineers, Software Engineers, Foundations Architects, Governance experts, Business Value Advisors, and Service Delivery Managers to deliver sustainable outcomes. • Balance speed, quality, cost, security, and maintainability while making clear technical and delivery trade-offs. • Define success criteria from the outset, including adoption, performance, reliability, risk, cost, and measurable business value. • Ensure solutions are documented, governed, and transferable so clients can operate them with confidence. • Turn successful delivery into reusable patterns, accelerators, and building blocks that strengthen future engagements.

• Design, develop, and deploy autonomous AI agents to automate virtual pre-construction, building plan interpretation, and estimation workflows. • Build and optimize agentic AI workflows that extract geometry, identify materials, and execute multi-step quantity estimation processes. • Develop AI-powered solutions that significantly reduce estimation turnaround times by automating repetitive and manual tasks. • Integrate modern AI technologies and LLM orchestration frameworks, including LangChain, LangGraph, Claude Code, AutoGen, and similar tools, into production-ready applications. • Apply robotics, computer vision, and spatial reasoning techniques to interpret 2D drawings and 3D digital twin models. • Design and enhance Human-in-the-Loop (HITL) workflows, enabling AI-generated outputs to be efficiently reviewed and validated by estimators. • Develop and maintain high-performance, scalable software using C++ and other performance-oriented technologies where appropriate. • Integrate AI workflows with cloud services, APIs, and internal platforms to ensure seamless end-to-end automation. • Optimize system performance, reliability, and accuracy while maintaining deterministic AI outputs. • Collaborate with product, engineering, and domain experts to design, test, and continuously improve AI-driven automation solutions. • Research, evaluate, and implement emerging AI technologies and best practices to enhance the platform's capabilities. • Document technical solutions, workflows, and system architecture to support ongoing development and knowledge sharing.