• Embed with a functional department for a defined engagement, running discovery to understand workflows and pain points before writing a line of code. • Scope, design, and build full-stack custom applications (not just automations or scripts) using modern frameworks, AI coding assistants, and internal platform APIs, moving from prototype to production quickly. • Own delivery end to end including architecture, build, testing, deployment, and the training and handoff that gets a department to actually adopt what you built. • Work hands-on in the code alongside AI coding tools (Claude Code, Codex, etc.) to compress build cycles, iterating with stakeholders in near real time rather than through long requirements documents. • Make explicit trade-offs between scope, speed, and quality as requirements shift, and communicate those trade-offs clearly to stakeholders and leadership. • Partner with IT, Security, and Data teams so every deployed application meets SentinelOne's standards for data security, compliance, and long-term supportability. • Codify recurring patterns, components, and playbooks from each engagement into reusable building blocks for the next department and for the broader engineering organization. • Feed field learnings back to platform, product, and data teams covering what worked, what broke, and what the next generation of internal tools should look like. • Measure and report success through production adoption and measurable business impact, not lines of code shipped or tickets closed.

• Release the function's pipeline of production tools — prioritized by business impact as the discovery pipeline surfaces them. • Engage directly with business stakeholders alongside the Solutions Lead — sit in on discovery conversations, ask the technical questions that surface real constraints, and translate what you hear into the right solution form. The Solutions Lead opens the door; the engineer brings the technical eye that decides what actually gets built. • Build headless, API-first, and agent-callable by default — every tool is engineered so a human can invoke it directly and an AI agent can invoke it programmatically as part of a larger workflow. API-first design, structured I/O, clean tool contracts. • Pair-program with an AI-paired development environment (Claude Code, Cursor, Copilot, or equivalent) — treat AI-paired development as the baseline mode of work, not an enhancement. Sustained throughput is the load-bearing promise of this role. • Contribute to a pattern catalog that compounds — document reusable patterns, tool contracts, and architectural decisions so each solution makes the next one cheaper to build. • Build internal tooling for the function’s own operations — including, over time, a FinOps agent that monitors AI usage across the function and surfaces cost-optimization opportunities. • Engineer with token-economics in mind — model routing (Haiku for retrieval and classification, Sonnet for reasoning), prompt caching, output validation, retry/cost discipline. Cost-aware code is good code. • Partner with Internal IT on the graduation pipeline — work alongside IT to harden tools that prove themselves and ensure handoff readiness when a tool graduates to production-grade managed infrastructure. • Practice security-conscious AI engineering — secrets in Bitwarden, environment hygiene, awareness of data exposure risks, and adherence to internal security and AI Spend Finance policy controls. • Document workflows, decisions, and reusable patterns so the work compounds across the Finance AI Transformation function rather than living in one person's head.

Role Description We're seeking an AI Software Engineer who is passionate about building production-grade AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI. This is a hands-on engineering role where you'll design, develop, deploy, and optimize secure AI solutions running on AWS. This is a fully remote, full-time position offering the opportunity to work on emerging AI technologies that support mission-critical public sector initiatives. Key Responsibilities - AI Application Development - Design, develop, and maintain AI-powered applications using Python. - Build intelligent applications leveraging OpenAI APIs, Amazon Bedrock, and other enterprise LLM platforms. - Design and implement Retrieval-Augmented Generation (RAG) architectures using enterprise knowledge sources and vector databases. - Develop Agentic AI workflows capable of autonomous reasoning, planning, and task execution. - Build reusable AI services, APIs, and software components for enterprise applications. - Optimize prompts, model interactions, and application performance for reliability, scalability, and cost efficiency. - Leverage AI development tools safely and effectively, taking ultimate human accountability for the design, security, and test coverage of all generated code. - Cloud & Infrastructure Engineering - Design, deploy, and maintain AI solutions on AWS. - Build Infrastructure as Code (IaC) using Terraform. - Develop containerized applications using Docker. - Deploy and manage workloads on Kubernetes. - Collaborate with DevOps engineers to automate deployments through CI/CD pipelines. - Ensure AI solutions meet security, scalability, and operational requirements. - Software Engineering & Integration - Develop secure REST APIs and backend services. - Integrate AI capabilities with enterprise applications, cloud services, and external APIs. - Write clean, maintainable, well-tested, and reusable code following software engineering best practices. - Participate in architecture reviews, code reviews, and technical design discussions. - Innovation & Research - Evaluate emerging AI frameworks, tools, and technologies. - Prototype new AI capabilities and rapidly validate proof-of-concepts. - Recommend improvements that increase developer productivity and enhance customer outcomes. - Help establish reusable AI patterns, templates, and best practices across projects. - Documentation & Collaboration - Create technical documentation, architecture diagrams, implementation guides, and operational runbooks. - Work closely with Solution Architects, Cloud Engineers, Product Managers, and customers to translate business requirements into AI solutions. - Participate in Agile ceremonies, sprint planning, backlog refinement, and knowledge-sharing sessions. Qualifications - Bachelor's degree in Computer Science, Software Engineering, Information Technology, or related discipline (or equivalent professional experience). - 4–8 years of professional software engineering experience. - 2+ years of professional Python development experience. - Must be a U.S Citizen or Green Card Holder. Requirements - Strong Python programming skills. - Efficient prompt engineering. - Experience building applications on AWS. - Experience integrating OpenAI APIs and/or Amazon Bedrock. - Hands-on experience implementing Retrieval-Augmented Generation (RAG) solutions. - Experience designing or developing Agentic AI workflows. - Experience developing RESTful APIs. - Experience using Terraform for Infrastructure as Code. - Experience with Docker and Kubernetes. - Experience using Git and modern CI/CD pipelines. - Strong understanding of software design principles, API development, and cloud-native architectures. Preferred Qualifications - Nice to have worked with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar AI orchestration frameworks. - Nice to have worked with vector databases such as Pinecone, Amazon OpenSearch, pgvector, or Weaviate. - Experience with Amazon ECS, Amazon EKS, Lambda, API Gateway, DynamoDB, or S3. - Familiarity with prompt engineering, AI evaluation, and model observability. - Experience with DevSecOps and secure software development practices. - Nice to have experience supporting U.S. Federal Government customers. - Familiarity with NIST 800-53, FedRAMP, FISMA, or CMS security requirements is a plus. What We're Looking For - Passion for solving complex business problems using AI. - Curiosity to learn and experiment with emerging AI technologies. - Strong analytical, troubleshooting, and problem-solving skills. - Excellent written and verbal communication skills. - Ability to thrive in a collaborative, fast-paced Agile environment. - Customer-first mindset with a commitment to delivering high-quality software solutions. Eligibility to Work at ArcheSys - All work must be performed within the continental United States. - Candidates must be legally authorized to work in the U.S. without sponsorship. - Selected candidates may be required to complete background investigations and obtain government security authorization for certain projects. - ArcheSys participates in E-Verify. What We Offer - Competitive salary and comprehensive benefits package. - Fully remote work environment. - Opportunity to build next-generation AI and cloud-native solutions. - Exposure to OpenAI, Amazon Bedrock, Agentic AI, and emerging AI technologies. - Continuous learning through certifications, training, conferences, and professional development. - Collaborative culture that encourages innovation, experimentation, and career growth. - Opportunity to support impactful Federal and Commercial digital transformation initiatives.

• Architecting modern web solutions. • Solving complex technical puzzles. • Collaborating with product managers, designers, and fellow engineers in fast-paced environments. • Taking ownership of features from design to production.