• Build and deploy AI-powered voice and messaging agents for healthcare and life sciences clients. • Own client-facing technical relationships. • Integrate with client data systems. • Write production backend services. • Engineer within HIPAA constraints. • Design and maintain databases. • Implement output guardrails ensuring compliance with healthcare regulations. • Collaborate with product, account management, and platform engineering.

Role Description The successful candidate will work as a team member to develop and utilize simulations and models. Responsibilities include: - Evaluating tools, technologies, architectures, models, and test results. - Heading meetings regarding AI with external parties and internal business-embedded innovation teams. - Collaborating with lead engineers to define subsystem requirements and acceptance criteria. The AI Engineer is responsible for designing, developing, testing, and maintaining artificial intelligence (AI) systems and applications. General responsibilities include: - Designing and implementing machine learning models using various programming languages such as Python, R, Java, JavaScript, or C++. - Collaborating with data scientists to develop algorithms for training AI models. - Conducting research on new technologies in the field of AI and applying them to improve existing systems. - Optimizing AI models for performance, scalability, and efficiency. - Integrating AI solutions into various software applications or platforms. - Collaborating with cross-functional teams such as product managers, UX designers, and data analysts to ensure the successful deployment of AI projects. - Ensuring that AI systems are secure, reliable, and adhere to ethical guidelines. - Staying up-to-date on industry trends and best practices in AI development. - Providing technical guidance and mentorship to junior engineers or interns. - Documenting code, models, and processes for future reference and collaboration with other team members. - Performing other related duties as required. Qualifications - BS Degree: AI Engineers candidates should hold a Bachelor's or Master's Degree in Computer Science, Electrical Engineering, Mathematics, Statistics, Robotics, Artificial Intelligence, Machine Learning, Data Science, or related technical field from an accredited university. - 1 to 3 years of relevant experience or any equivalent combination of relevant education and experience. - Current U.S. Citizenship is required. - DoD Secret security clearance. - Strong written and verbal communication skills. - Strong Microsoft Office skills. Company Description Arcarithm is committed to investing in our employees by offering comprehensive health insurance options, a generous 401K plan, competitive salaries, continuous career growth opportunities, flexible schedules including remote work, mentoring, and performance incentives. We are located in beautiful, downtown Huntsville, AL, one of the fastest growing cities in the U.S.! We cultivate and foster an environment of integrity, open communication, work-life balance, and career development. We are excited to continue to change and improve the world through innovation and technology!

• Help build the foundation for GitLab's transformation into an AI-first company • Diagnose business problems before building solutions • Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation • Design, develop, and ship AI-powered solutions quickly • Improve organizational flow by building solutions that reduce bottlenecks • Integrate AI capabilities into existing systems and workflows using APIs • Be Customer Zero: leverage and showcase GitLab's AI offerings • Partner closely with stakeholders across functions • Define and track success through business metrics

Role Description As a Sr. Delivery Acceleration AI Engineer, you will design, develop, and optimize AI-powered autonomous implementation solutions that transform how ServiceNow Expert Services deliver customer implementations. Working within the Delivery Acceleration team, you’ll focus on building and refining AI agents that automate ServiceNow configuration, generate implementation artifacts, and accelerate time-to-deploy—turning what previously required weeks of manual effort into AI-driven delivery that produces production-ready outputs in hours. This role requires deep expertise in prompt engineering, AI agent architecture, and an understanding of enterprise professional services delivery. You’ll work directly with autonomous implementation platforms built on large language models to create, test, and refine the AI-driven workflows that generate ServiceNow configurations, user stories, test scripts, and deployment packages for customer engagements. Critically, you must understand the professional services go-to-market motion—how services are scoped, sold, estimated, and delivered—because the AI solutions you build must align with how consultants and sales teams position and execute customer engagements. Your work directly impacts delivery speed, quality, margin, and the customer experience. Success requires balancing technical depth in AI/LLM systems with practical business acumen. You’ll operate in a fast-paced sprint cadence where AI capabilities evolve rapidly, requiring continuous experimentation, rigorous quality validation, and close collaboration with solution architects, product managers, and delivery consultants across AMS, EMEA, and APAC+JPN. You will partner closely with the platform administrator who owns operations, adoption, and governance for the AI-powered deal and delivery document generator, ensuring the AI agents and prompt assets you build are deployed, scaled, and governed effectively across the global delivery community. What You Get to Do in This Role: - Design & Develop AI-Powered Implementation Agents - Architect and build AI agent workflows that autonomously generate ServiceNow configurations, implementation plans, user stories, and test scripts from customer requirements. - Develop, test, and iterate sophisticated prompt chains and orchestration patterns that produce consistent, production-quality outputs across ServiceNow product workflows (ITSM, CSM, HRSD, SPM, etc.). - Design multi-step agentic processes that handle complex implementation logic—scope validation, dependency mapping, configuration generation, and quality assurance—with minimal human intervention. - Build and maintain prompt libraries, templates, and reusable patterns that encode delivery best practices and Gold Implementation standards into AI agent behavior, partnering with the platform administrator on library structure, versioning, and publication to end users. - Partner with AI Architects and Solution Architects to ensure agent outputs align with ServiceNow platform strategy, product direction, and enterprise readiness standards. - Lead Prompt Engineering & AI Quality Optimization - Serve as a subject matter expert in prompt engineering techniques for large language models, including chain-of-thought reasoning, few-shot learning, structured output generation, and context window optimization. - Design and execute systematic prompt evaluation frameworks that measure output accuracy, consistency, completeness, and adherence to ServiceNow implementation standards. - Continuously optimize prompt performance through A/B testing, output analysis, and iterative refinement—treating prompts as production code with version control and quality gates, incorporating user feedback and quality-audit signals surfaced by the platform administrator and the champions network. - Develop guardrails and validation logic that ensure AI-generated configurations meet enterprise quality standards before being deployed to the Expert Services community. - Stay current on evolving platform capabilities and techniques, rapidly incorporating new approaches that improve agent accuracy and efficiency. - Integrate AI Solutions with Professional Services GTM & Delivery - Understand the full professional services lifecycle—from pre-sales scoping and estimation through delivery execution and go-live—and ensure AI agent capabilities map to real engagement workflows. - Design AI solutions that support accurate estimation by providing data on automated vs. manual effort, enabling services sales teams to right-size engagements. - Partner with delivery consultants to validate that AI-generated outputs are usable in real customer contexts, incorporating feedback into continuous improvement cycles. - Contribute to packaging AI-accelerated delivery into repeatable service offerings that can be positioned and sold by services and license sales teams. - Build Platform Integrations & Delivery Automation - Integrate AI agent outputs with the ServiceNow platform, customer engagement portals, and internal delivery systems to create seamless end-to-end automation. - Design APIs, data pipelines, and integration patterns that connect autonomous implementation tools with estimation systems, resource management platforms, and project tracking tools. - Build automated testing and validation workflows that verify AI-generated configurations against ServiceNow best practices and customer requirements before deployment. - Collaborate with the implementation platform team to embed AI capabilities into the customer and partner delivery experience. - Ensure all integrations meet enterprise security, data governance, and compliance standards. - Coordinate with the platform administrator on release management—staging validation, production rollout, and vendor escalation—so new AI capabilities reach users without disrupting platform stability. - Drive Measurement & Continuous Improvement - Establish quality metrics for AI agent outputs: accuracy rates, rework percentages, time savings, and customer acceptance rates, feeding these signals into the adoption and ROI metrics the platform administrator reports to A&M leadership. - Analyze agent performance data to identify improvement opportunities, failure patterns, and expansion use cases. - Contribute to the delivery acceleration roadmap by identifying where AI can fill capability gaps or replace manual processes. - Document engineering best practices, architectural patterns, and lessons learned to build organizational knowledge. - Participate in sprint ceremonies, code reviews, and cross-team collaboration in a two-week sprint cadence with monthly releases. Qualifications - 5+ years of experience in software engineering, AI/ML engineering, or technical consulting in enterprise software environments. - 2+ years of hands-on experience with large language models, prompt engineering, and AI agent development—not just using AI tools, but building production systems on top of them. - Demonstrated expertise in prompt engineering techniques: chain-of-thought reasoning, structured output generation, few-shot/zero-shot patterns, context window management, and systematic prompt evaluation. - Strong understanding of professional services delivery operations: how engagements are scoped, sold, estimated, staffed, and delivered. - Experience with API design, system integration, and building data pipelines that connect AI outputs to enterprise workflows. - Proficiency in Python, JavaScript/TypeScript, or similar languages used in AI agent development and automation. - Understanding of ServiceNow platform architecture, data models, and configuration patterns. - Excellent communication skills—ability to translate technical AI capabilities into business impact for non-technical stakeholders. - Experience working in agile, sprint-based environments with measurable delivery outcomes. Requirements - Not required but nice to have: - Experience building autonomous implementation or code generation systems that produce production-ready outputs. - Background in management consulting or professional services delivery operations. - Familiarity with ServiceNow implementation methodology and delivery best practices. - Experience with AI agent orchestration frameworks and multi-step workflow automation. - Knowledge of AI ethics, output validation, responsible AI practices, and enterprise governance requirements. - ServiceNow platform certifications (CSA, CIS, or similar). - Experience with enterprise estimation tools, resource management platforms, or delivery automation systems. Company Description It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.