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Jaroměř, Česko
Remote only
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Workforce AI Solutions Engineer based in the United States.
The Senior Workforce AI Solutions Engineer will design, build, and deploy enterprise AI solutions that improve employee productivity, streamline workflows, and deliver measurable business value. Working closely with business stakeholders and technical teams, this role transforms qualified requests into secure, practical, and maintainable AI-powered applications. The position combines solution architecture, hands-on engineering, workflow automation, and AI agent development using enterprise platforms such as Microsoft Copilot, Copilot Studio, Claude, and ChatGPT Enterprise. The successful candidate will manage solution requests from initial scoping through implementation, testing, documentation, and handover. They will also develop reusable agents, integrations, and engineering patterns that accelerate adoption across the organization. This is a full-time remote opportunity for a technically versatile professional who balances innovation with enterprise security, governance, and responsible AI practices.
Manage AI solution requests: Own and prioritize a queue of qualified business requests, coordinating with the intake team and a delivery colleague to clarify requirements, confirm scope, identify dependencies, and determine appropriate implementation approaches.
Lead end-to-end delivery: Guide each engagement from requirements gathering and solution design through development, testing, deployment, documentation, and transition to ongoing support.
Translate business needs into AI solutions: Work directly with requesters to understand workflows, data requirements, desired outcomes, potential risks, and success criteria, then translate these needs into practical technical solutions.
Design AI-powered workflows and applications: Build AI agents, automations, workflow enhancements, knowledge retrieval solutions, document intelligence capabilities, and system integrations using approved enterprise technologies.
Select appropriate implementation methods: Choose between low-code builders, automation platforms, and pro-code development based on business requirements, technical complexity, security, maintainability, and cost.
Develop reusable AI components: Create and maintain agents, actions, prompts, skills, orchestration logic, templates, connectors, and Model Context Protocol (MCP) components that can be reused across business functions.
Build custom integrations: Extend AI platforms using APIs, SDKs, custom connectors, functions, and other approved integration methods when built-in capabilities are insufficient.
Secure enterprise data access: Design integrations with business applications, knowledge repositories, and data sources using appropriate authentication and authorization methods, including OAuth, Microsoft Entra ID, service principals, managed identities, and delegated access.
Protect information and maintain compliance: Apply least-privilege access, data protection, privacy, security, and governance requirements throughout data retrieval, processing, storage, and output generation.
Apply sound engineering practices: Use source control, automated testing, documentation, release management, reusable code, and lifecycle planning to ensure solutions are reliable, observable, scalable, and supportable.
Maintain platform expertise: Stay current with Microsoft Copilot, Copilot Studio, Claude, ChatGPT Enterprise, associated APIs, and emerging enterprise AI capabilities; assess their strengths, limitations, licensing, and integration options.
Improve delivery practices: Evaluate new technologies, share lessons learned, and collaborate with AI enablement, platform, security, architecture, and engineering teams to refine standards and accelerate implementation.
Track outcomes and business value: Monitor request turnaround, stakeholder satisfaction, solution adoption, productivity gains, service quality, and process improvements to demonstrate the impact of delivered solutions.
Communicate progress and manage expectations: Estimate effort, document technical decisions, maintain visibility into delivery status, resolve issues, and communicate realistic timelines and outcomes to stakeholders.
Relevant technical experience: Background in solution architecture, software engineering, automation, cloud engineering, application integration, or a related technology discipline.
End-to-end solution delivery: Demonstrated experience translating business requirements into technical solutions and delivering them through implementation.
Enterprise AI platforms: Hands-on experience with one or more enterprise AI technologies, such as Microsoft Copilot, Copilot Studio, Claude, ChatGPT Enterprise, or comparable platforms.
Integration and development skills: Experience working with APIs, SDKs, automation technologies, integrations, and structured or unstructured enterprise data sources.
Identity and security knowledge: Working knowledge of enterprise authentication, authorization, identity management, and secure integration patterns.
Low-code and pro-code development: Ability to build solutions using both low-code platforms and traditional development tools, selecting the right approach for each use case.
AI agent development: Experience building custom agents, AI assistants, agent actions, or multi-step AI workflows is preferred.
MCP and platform extensibility: Familiarity with MCP servers, custom skills, plugins, connectors, or comparable extensibility components is preferred.
Microsoft ecosystem expertise: Experience with Microsoft Graph, REST APIs, Azure Functions, Logic Apps, Power Automate, GitHub, Visual Studio, or Visual Studio Code is an advantage.
Enterprise knowledge retrieval: Familiarity with retrieval-augmented generation (RAG), enterprise search, grounding, vector search, and knowledge management patterns is preferred.
Responsible AI practices: Understanding of AI governance, data protection, security, compliance, auditability, and responsible AI implementation.
Business value measurement: Experience identifying and measuring improvements in productivity, process efficiency, service quality, or business outcomes is beneficial.
Analytical problem-solving: Strong troubleshooting, analytical, and solution-design skills, with the ability to evaluate trade-offs and simplify complex problems.
Stakeholder communication: Excellent consulting, documentation, and communication skills, including the ability to explain technical concepts clearly to business and technical audiences.
Organization and accountability: Ability to manage multiple requests, balance business urgency against technical risk, prioritize effectively, and drive work to completion with minimal supervision.
Adaptability and curiosity: A proactive, learning-oriented mindset and enthusiasm for emerging AI capabilities, combined with disciplined engineering judgment and a strong sense of ownership.
Annual base salary: $100,000–$140,000, depending on location, relevant knowledge, skills, and experience.
Short-term incentive: Eligibility for a short-term incentive program.
Healthcare coverage: Employee health insurance benefits.
Retirement benefits: Access to a retirement plan.
Disability benefits: Disability coverage as part of the employee benefits package.
Paid time off: Access to a paid time off program.
Remote flexibility: Full-time remote work arrangement within the United States.
AI innovation: Opportunity to work with enterprise AI platforms, agent development, workflow automation, and emerging integration technologies.
Business impact: Directly contribute to improved employee productivity, streamlined processes, and measurable operational outcomes.
Professional growth: Opportunities to expand expertise across AI engineering, cloud integrations, enterprise architecture, and responsible AI practices.
Collaborative environment: Work with business stakeholders and cross-functional technical teams to develop practical solutions and reusable capabilities.
