• Take scoped use cases from the Director, AI Enablement and build working solutions: prompt layers, automation flows, lightweight agents, RAG implementations, and API integrations that connect AI tools to the systems business teams already use • Build working prototypes quickly - the goal is to show a team what is possible in days, not weeks, then refine based on what actually lands • Translate a documented workflow assessment into a technical design, select the right tools for the job, and own the build through to a deployable solution • Maintain solution documentation that is clear enough for handoff, iteration, and audit - not just for engineers, but for the business owner who will live with it • Spend time inside the functions you are building for - Finance, HR, Legal, Marketing Ops, Customer Success, and others - understanding how the work actually flows before you redesign it • Run working sessions with team members to map current-state workflows, surface friction points, and identify where AI creates real leverage versus where it creates noise • Build credibility with non-technical stakeholders by showing them working solutions, not decks - and by being honest about what will and will not work • Support user acceptance testing and early adoption, troubleshooting issues close to launch and iterating based on real usage • Build demos and proof-of-concepts that help the Director, AI Enablement make the case for a use case - or disqualify it early before resources are committed • Contribute to vendor and tool evaluations with a builder's perspective: not just whether a tool claims to do something, but whether it actually works in the context of a specific workflow • Feed learnings from the build process back into the program - what worked, what did not, what should be standardized across functions • Work on tactical quick wins and longer-horizon strategic builds at the same time, switching contexts across active use cases as priorities shift

Role Description We are looking for a highly skilled AI Systems Engineer who will be part of a collaborative and agile team that supports and builds modern, usable, and responsive applications for mission-critical U.S. federal government health IT solutions. The Senior AI Systems Engineer will operate in a fast-paced innovation environment, rapidly developing and validating proof of concepts for advanced AI and machine learning use cases. The role centers on: - Experimenting with cutting-edge analytics tools - Integrating them into a cloud-based analytics ecosystem - Helping move successful concepts toward operational deployment - Hands-on data exploration - Prototyping - Workflow automation - Contributing to secure, well-governed MLOps practices The Software Engineer will work closely with engineering and product teams to: - Evaluate emerging tools - Build technical demonstrations - Document integrations - Communicate platform updates in a highly collaborative setting Qualifications - 5+ years of relevant software / systems engineering experience - Software engineering background with deep experience building production-grade applications and services - Expertise developing agentic AI systems, including planning, tool use, multi-step reasoning, workflow execution, or autonomous decisioning logic - Hands-on experience designing and implementing MCP based integrations, tool interfaces, or model-driven service frameworks - Ability to translate ambiguous business or mission requirements into scalable AI-driven solutions, balancing technical feasibility with real-world impact - Proficiency with LLM development practices including fine-tuning, RAG integration, prompt engineering, and interaction models for agent workflows - Strong Python development skills and familiarity with distributed compute environments, APIs, microservices, and cloud-native architectures - Experience integrating agents or LLM driven components into cloud platforms (Azure, AWS, GCP) or large scale data ecosystems - Understanding of LLMOps/MLOps principles including versioning, testing, deployment automation, monitoring, and governance for agentic systems - Demonstrated ability to lead solution design, mentor developers, and communicate complex AI architectures to technical and non-technical stakeholders - Experience with version control and modern CI/CD practices (e.g., Git/GitHub), including automated testing, deployment pipelines, and release management for production systems - Ability to obtain/maintain a Public Trust clearance Requirements - Experience building or contributing to multi-agent systems or coordinated agent workflows - Familiarity with frameworks such as LangChain, LlamaIndex, Strands Agents, LangGraph, CrewAI - Knowledge of evaluating agent performance, implementing guardrails, or designing safe action execution patterns - Hands-on work with vector databases, embedding models, or advanced retrieval techniques - Experience integrating agentic components into large scale analytics or data platforms (e.g., Databricks, Snowflake) - Background in data exploration or data modeling to support agent-driven decision processes - Experience building AI focused prototypes or experimenting with emerging agentic patterns in rapid iteration environments - Familiarity with optimization techniques such as model routing, response validation layers, or inference acceleration - Familiarity with systems that handle sensitive data in accordance with HIPAA and federal security standards, including implementation of encryption, access controls, auditability, and governance for protected health information (PHI) within AI/LLM workflows Benefits - Top-tier benefits package to invest in your physical, mental, and financial health and wellness - Opportunities to learn new skills, seize new challenges, and advance your career - A culture of inclusion and opportunity for all

• Designing, building, testing, and delivering production AI capabilities, including intelligent agents, workflows, MCP servers, integrations, machine learning models, and shared services • Developing AI-powered products that automate clinical, operational, financial, and patient-facing workflows across the Curve platform • Building, training, evaluating, deploying, monitoring, and continuously improving machine learning models and intelligent services running in production • Developing reusable MCP tools, APIs, and platform components that accelerate AI development across multiple platform teams • Writing high-quality production code while leveraging modern AI-assisted development tools, including Claude Code, to improve software quality and development productivity • Participating in technical design discussions, code reviews, testing, production deployments, and production support throughout the software development lifecycle • Working closely with Product, UX, Architecture, and development teams to transform business requirements into secure, scalable, maintainable software • Continuously improving AI capabilities through experimentation, prompt engineering, model tuning, evaluation, monitoring, and operational feedback • Taking ownership of production software by troubleshooting complex issues, improving reliability, and helping deliver exceptional customer experiences

• Develop and integrate LLM-based AI solutions via Copilot Studio & Prompt Flow; deploy AI agents into Teams, SharePoint, and Power Platform workflows. • Architect and deploy Microsoft 365 services (Exchange Online, OneDrive, Teams, SharePoint Online, Intune, M365 Apps) with scalable, secure designs. • Implement security & compliance features (Purview, Responsible AI) across M365 environments. • Automate tasks using PowerShell and build custom solutions with Power Automate & Power Apps. • Act as a trusted advisor for clients by leading technical workshops, delivering proofs of concept (POCs), and presenting AI and automation solutions with a clear, structured, and high‑impact communication style tailored to both technical and executive audiences. • Strong knowledge of Azure AI Foundry and Copilot Studio, with the ability to scale, operationalize, and govern AI solutions across enterprise environments, ensuring performance, security, and reliability as adoption grows.