• Support and mentor developers in designing and implementing project work, ensuring high-quality deliverables. • Collaborate with designers and Product Managers to estimate, prioritize, and plan work within an Agile framework. • Maintain transparency by proactively updating stakeholders on progress, risks, and technical decision-making. • Create clear and maintainable documentation and diagrams that communicate complex solutions to clients and teammates. • Engage directly with clients to understand requirements, provide updates, and address questions with clarity and professionalism. • Write clean, efficient, well-documented code following industry best practices. • Participate in code reviews to uphold high-quality, scalable, and consistent codebases. • Leverage modern AI-powered and agentic developer tools (e.g., Claude Code, Devin, GitHub Copilot, Codex) to improve velocity, quality, and creativity in problem-solving. • Evaluate when and how to apply AI-assisted development responsibly, balancing efficiency with sound engineering judgment. • Explore opportunities to integrate automation and AI into workflows (e.g., test generation, scaffolding, documentation, debugging assistance, architecture exploration). • Contribute to shaping Orium’s internal adoption of AI for software development and share learnings with the team.

• Be the technical lead and owner of significant scope, working through ambiguity, concept validation and implementation of a best-in-class solution. • Build our platforms--infrastructure, applications and tools using your expertise in distributed systems, large compute clusters and petabyte-scale storage infrastructure. • Help build out the team, cultivate innovation and growth by driving cross-collaboration and execution of projects across multiple teams. • Be a player/coach of our high-performance team to ensure code quality, commitment to craft and operational excellence. • Scale distributed applications in a highly-available environment, make architectural trade-offs applying design patterns and disciplined execution. • Work with cross-functional teams with design, product, data science, and research partners to drive engineering decisions and influence outcomes.

• Design, develop, and maintain full-stack features across backend services, APIs, frontend, and database layers • Work primarily on backend systems (~80%) with some frontend responsibilities (~20%) • Contribute to and modernize legacy VB.NET MVC applications while supporting the migration to .NET Core • Build and integrate AI-assisted development workflows using tools such as ChatGPT, Claude, or similar technologies • Collaborate directly with clients to gather requirements, deliver enhancements, and provide technical solutions • Write and optimize SQL queries, stored procedures, and database interactions in SQL Server • Participate in Agile development processes using tools like JIRA and Git/TFS

• Design and ship a robust, end-to-end AI evaluation framework, covering offline evals, production tracing, and human-in-the-loop feedback loops, connected across all of Lattice’s AI use cases. • Define and instrument the metrics that actually matter: agent task completion, hallucination rates, response quality, user engagement, and downstream business outcomes. • Build and maintain evaluation datasets, test harnesses, and automated scoring pipelines to catch regressions before they ship. • Identify and surface the drivers of agent quality improvement, giving the team clear signals on where to invest. • Architect and implement reusable agent infrastructure: multi-turn conversation workflows, recommendation services, LLM DAGs, and standardized agent topology patterns using LangGraph. • Build and scale RAG pipelines and retrieval infrastructure, including vector store management and retrieval quality optimization. • Make principled build vs. buy decisions across LLM providers, agent frameworks, and evaluation tooling, balancing capability, cost, latency, and vendor risk. • Contribute to production AI systems with a strong focus on reliability, observability, and performance, not just prototypes. • Own projects end-to-end: scope them, drive them to completion, and bring in the right people at the right time. • Partner with engineering leads and managers to inform technical direction on agent quality and evaluation strategy you’ll be expected to hold intelligent, substantive conversations about methodology, not just implementation. • Raise the AI engineering bar across the broader team through code review, documentation, and thoughtful technical debate.