• Oversee, influence, and drive execution of large-scale strategic initiatives across Veralto’s Operating Companies • Build PMO, governance, structure and frameworks that ensure programs are delivered on time, on budget, and aligned to business objectives • Support the development of scalable, organization-wide frameworks and delivery models for AI, data, and emerging technology initiatives that drive value and positively impact P&L • Partner with operating company leadership to oversee execution of enterprise-wide strategic initiatives and transformation programs • Ensure programs remain on track across scope, timeline, budget, risks, and business outcomes • Establish governance structures, reporting cadences, and escalation mechanisms • Drive cross-functional alignment across business, IT, operations, and external partners • Partner with consulting firms, SIs, and service providers supporting enterprise initiatives • Support executive review of partner performance and delivery effectiveness • Help catalog and organize enterprise efforts to reduce duplication and identify scalable opportunities • Develop executive-level reporting, dashboards, and strategic updates

Role Description AI is rewriting how software gets built and most organizations are still bolting chatbots onto yesterday's process. We're doing something different. UKi is looking for an AI Workflow Engineer to architect our agentic SDLC from the ground up: - Formalized pipelines where AI agents plan, build, test, review, and ship alongside a core team of skilled senior US-based engineers. - Governance, grounding, and measurement to prove it works. This isn't a research role and it isn't prompt tinkering. You'll be designing the delivery system itself: the workflows, guardrails, and feedback loops that let a high-performing team move dramatically faster without sacrificing the engineering excellence we practice every day. Your work will directly accelerate a mission that matters: delivering workforce readiness to the nation's cyber warfighters. Agentic development is the biggest shift in how software gets built since continuous delivery itself. Very few teams get to design and own that transformation from the ground up, backed by senior engineers who want it to succeed, in service of a mission that protects the people defending the nation. If you've been waiting for the role where AI engineering meets real delivery discipline and real impact, this is it. Qualifications - 2–3+ years of hands-on experience building with AI/LLM systems in production settings. - 2+ years designing and operating agentic workflows and pipelines including multi-step agent orchestration, tool use, evaluation, and governance. - Experience embedded in modern development teams practicing GitOps and CI/CD (required). - Working knowledge of grounded AI patterns: RAG, context engineering, structured outputs, and evaluation/observability for LLM systems. - Solid software engineering fundamentals in JavaScript/TypeScript and/or Python; comfort working in and around our stack (Angular/React frontends on Express web applications). - Genuine alignment with continuous delivery culture: the Allspaw/Hammond, Phoenix Project (Kim) school of small batches, fast feedback, blameless learning, and operational excellence. - A self-starter's bias for action: you see an opportunity to accelerate the team and you build it, measure it, and share it. - Strong communication skills: you can explain an agentic architecture to an engineer and its ROI to an executive. - Must be authorized to work in the United States; role is US-remote. Requirements - Experience with agent frameworks and protocols (e.g., LangGraph/LangMem, Claude Agent SDK, MCP, OpenAI Agents) and AI coding tools (Claude Code, Copilot, Cursor). - Building developer platforms, internal tooling, or platform engineering / DevEx functions. - DORA-style delivery metrics and engineering analytics. - Familiarity with AI governance frameworks (NIST AI RMF) and secure SDLC practices. Benefits - Base salary range: $150,000–$200,000, commensurate with experience, plus benefits. - This range reflects current market rates for mid-level AI/agentic engineering talent in US-remote roles. Company Description Since 1999, Ultimate Knowledge Institute has set the standard for cybersecurity training and readiness for the Department of Defense, federal agencies, and Fortune 500 companies. Today we're delivering a platform that builds and demonstrates true workforce and work-role readiness. Our platform identifies individual expertise gaps and builds adaptive, custom training plans to close those gaps for Federal U.S. Government, Defense, Intelligence Community, State/Local, and commercial customers. You'll be joining a newly forming AI engineering team inside a mature product engineering team, which means real users, real stakes, and a green field for how AI transforms the way we build. Our Tech Environment Angular and React frontends on Express/Node.js web applications, delivered through GitOps and automated CI/CD pipelines. We practice continuous delivery: deploy often, automate everything, learn from every incident. You'll have wide latitude to introduce the agentic tooling and AI infrastructure this mission needs.

Role Description Our client is hiring an AI Evaluation Lead to own how we measure the quality of AI-generated financial advice. Getting the advice right matters. A bad output here has real consequences for real people, and this role owns making sure we catch it. - You will work with an AI-generated test case library and automated scoring infrastructure that is already in place. - Your job is to make sure we are measuring the right things, interpreting what the results are telling us, and determining what needs to change to keep the system performing well as it scales. - This is not a monitoring and reporting role. It requires genuine judgment about AI system behavior, advice quality, and what the data is and is not capturing. - You will report to our Head of Revenue & Compliance, work closely with the AI/ML team and founders, and partner with subject matter experts who provide domain judgment on complex or ambiguous cases. - You need enough personal finance literacy to make first-pass quality assessments independently and know when to escalate. Qualifications - You have worked on AI or ML system quality in a context where outputs had real stakes. - You think analytically about what data is and is not telling you. - You are comfortable making judgment calls in ambiguous situations rather than waiting for the answer to be obvious. - You have enough AI/ML fluency to reason about why a system is producing what it is producing, not just whether the output looks right. - You bring enough personal finance literacy to read an advice response and have a genuine opinion about whether it is directionally sound. - You do not need formal credentials or deep expertise across every domain the system covers—you will partner with subject matter experts for the complex judgment calls. - Your review is substantive rather than mechanical, and you can have an informed conversation with those experts about what you are seeing in the data. - Fluency with how LLM-based systems behave in production, including output variance, failure modes, and the limits of automated scoring. - Ability to assess whether an eval framework is measuring the right things, not just whether it is running correctly. - Comfortable working with behavioral and interaction data to surface patterns and quality signals. - Familiarity with evaluation and observability tooling. Requirements - Model evaluation or QA on a consumer-facing AI product, particularly in a regulated or high-stakes context. - Model risk or validation with LLM or generative AI exposure. - Data science or analytics with ownership of production AI system quality. - Operations quality control built around AI- or ML-generated outputs. - Financial services or fintech product roles where you developed both analytical depth and personal finance domain familiarity. Benefits - Salary: 120-140k, plus early-stage option equity. - Final compensation will depend on level, experience, location, and scope of responsibility. - This role is open to candidates based in the United States. How we work - We are a fully remote, distributed team. - Periodic in-person get-togethers will be integral to our operating cadence. - We prioritize outcomes and output over set schedules. - We value clear writing, high ownership, fast iteration, direct communication, and thoughtful async collaboration. - As an early team member, you should expect broad ownership, frequent context shifts, and a high degree of autonomy. - You will help shape not just the product, but also the technical standards and operating cadence of the company. AI Interview We expect a high volume of applications for this role. To help candidates showcase more than what's on their resume, you'll have the opportunity to complete an AI interview as part of the application process. As an AI-first company, we embrace AI throughout the hiring process and are excited to meet candidates who are equally curious about and enthusiastic about the technology. This interview is your chance to demonstrate your experience, communication skills, and potential beyond your resume.

• Conduct insider threat investigations involving cloud environments and AI-enabled technologies, analyzing SaaS activity, system logs, user behavior, and AI tool interactions to identify risk. • Identify and investigate emerging insider threat patterns, including data exposure in cloud platforms and misuse of generative AI tools. • Contribute to the development and enhancement of investigative playbooks, workflows, and methodologies specific to cloud and AI-related risks. • Partner with Cybersecurity, IT, Legal, Compliance, HR, Privacy, and Analytics teams to ensure investigations align with regulatory, legal, privacy, and internal control requirements. • Translate investigative findings and emerging risk insights into recommendations for process, control, and technology improvements to strengthen the organization’s risk posture.