AI Solutions Implementation Specialist

Summary

Loftware provides cloud-based labeling solutions that help businesses manage labeling across their operations and supply chains. The company is seeking an AI Solutions Implementation Specialist to build and improve AI prototypes and MVPs using low-code platforms, translate business needs into technical specifications, and collaborate with engineering, product, architecture, and business teams to scale validated solutions and support AI adoption.

Responsibilities

• Design, build, and iteratively improve functional prototypes and MVPs for AI use cases using internal, low-code/no-code AI platforms (e.g., Microsoft Copilot, Copilot Studio, Power Platform, or similar)
• Translate business and stakeholder requirements into clear technical specifications, user stories, and acceptance criteria that engineering teams can build from
• Partner with engineers throughout the development handoff, from prototype to production, to ensure validated ideas scale into reliable, supportable features
• Evaluate, select, and roll out AI-driven productivity tools, running pilots and tracking adoption and measurable impact
• Support go-to-market and customer success teams by building lightweight AI-assisted workflows (e.g., sales enablement aids, support automation) using internal AI tooling
• Apply a working understanding of AI data security, privacy, and governance principles when building tools, partnering with the CISO and AI Innovation Committee on anything requiring deeper security review
• Work with enterprise architects to ensure the tools and models you build on are consistent with approved AI platforms and frameworks
• Stay current on emerging low-code/no-code AI tooling and enterprise copilot capabilities, sharing opportunities and risks with the broader team

Skills

• Experience building prototypes, MVPs, or internal tools using low-code/no-code platforms (e.g., Microsoft Power Platform, Copilot Studio, or similar)
• Ability to translate business requirements into clear specifications, workflows, or user stories that engineering teams can act on
• Working knowledge of generative AI/LLM concepts and enterprise AI copilot tools (e.g., Microsoft Copilot) and how they apply to business problems
• Basic understanding of data security, privacy, and governance principles as they apply to AI tools (deep security expertise not required)
• Experience collaborating cross-functionally with engineering, product, and business stakeholders; strong written and verbal communication skills
• Hands-on experience with Microsoft Copilot Studio, Power Automate, Power Apps, or similar internal/low-code AI tooling
• Familiarity with prompt engineering and practical, real-world applications of LLMs
• Experience running pilots or proof-of-concepts and measuring tool adoption and impact
• Exposure to compliance and risk considerations for AI (e.g., data residency, model governance): basic literacy, not expert-level
• No professional software engineering or coding experience is required, though comfort with technical concepts (APIs, data structures, basic logic) is a plus

Qualifications

Must Haves

• Experience building prototypes, MVPs, or internal tools using low-code/no-code platforms (e.g., Microsoft Power Platform, Copilot Studio, or similar)
• Ability to translate business requirements into clear specifications, workflows, or user stories that engineering teams can act on
• Working knowledge of generative AI/LLM concepts and enterprise AI copilot tools (e.g., Microsoft Copilot) and how they apply to business problems
• Basic understanding of data security, privacy, and governance principles as they apply to AI tools (deep security expertise not required)
• Experience collaborating cross-functionally with engineering, product, and business stakeholders; strong written and verbal communication skills

Nice to Haves

• Hands-on experience with Microsoft Copilot Studio, Power Automate, Power Apps, or similar internal/low-code AI tooling
• Familiarity with prompt engineering and practical, real-world applications of LLMs
• Experience running pilots or proof-of-concepts and measuring tool adoption and impact
• Exposure to compliance and risk considerations for AI (e.g., data residency, model governance): basic literacy, not expert-level
• No professional software engineering or coding experience is required, though comfort with technical concepts (APIs, data structures, basic logic) is a plus

Benefits

• Comprehensive training for all employees
• An emphasis on employee development