AI Implementation Lead
Position Summary
The AI Implementation Lead is the Data Team's hands-on builder, deploying AI solutions where Data Team work meets the business. The ideas are coming faster than the implementation; this role exists to close that gap. The work spans agents, plug-ins, applications, integrations, and whatever form the use case calls for. You will scale IEM's existing AI-native infrastructure (Claude Code in daily use, multiple local MCP servers wired to production data systems, an internal multi-agent portfolio across analytics and operations, and an established AI-assisted development culture) from a director-led prototype into a Data Team production platform. This role works in coordination with IEM's Enterprise AI team, which sets enterprise AI strategy and policy; the AI Implementation Lead applies that framework to the solutions you build rather than authoring it. This is the first dedicated AI engineer on the Data Team, offered with track flexibility as either a principal individual contributor or a hands-on people leader managing 2 to 4 engineers as the function scales.
Ideal Candidate Profile
You have 5+ years of software engineering experience with hands-on AI and agentic system development. You have built agents, plug-ins, or AI applications that real organizations have used, not coursework or POCs. You think first about the use case and the user, then about the system, then about the model. You write production code yourself and pride yourself on building solutions that actually get adopted. You partner naturally with non-technical stakeholders, translating fuzzy AI ideas into deployed solutions and surfacing the questions behind the questions. You are comfortable working with modern agent tooling such as Claude Code, MCP, LangGraph, Mastra, or Pydantic AI, and you treat AI-assisted development as a daily multiplier for engineering, governance, documentation, and adoption work. You have a working knowledge of AI governance and security including prompt injection mitigation, data leakage prevention, and model risk. You are excited about defining a new function inside a Data Team that already has the foundation in place.
Key Responsibilities
• AI Solution Deployment: Build agents, plug-ins, applications, and integrations as use cases emerge from the Data Team's work and the partners it supports
• Agent Development: Design and develop custom agents and agent harnesses, including MCP servers, orchestration logic, prompt engineering, and eval scaffolding. Agents are the centerpiece of the function.
• Vendor AI Extension: Build on top of Salesforce Einstein, Tableau Pulse-AI, ETQ-AI, Fellow.ai, and the data-platform AI capabilities IEM brings on next
• Solution Discovery: Run intake conversations with internal partners to surface high-value AI use cases, pressure-test feasibility against existing tooling, and prioritize the implementation roadmap
• AI Governance and Security: Build with data leakage prevention, prompt injection mitigation, and model risk as first-class concerns, in alignment with IEM's broader AI policy framework
• Adoption and Enablement: Train users, write documentation, and run enablement programs so the AI solutions you deliver get used by the people they were built for
• Cross-Functional Delivery: Coordinate with the internal teams the Data Team serves through build and rollout, keeping stakeholders aligned as solutions move from idea to deployment
• Documentation: Maintain design notes, integration docs, and runbooks for the AI solutions you deliver so the team can support and extend your work
• AI-Assisted Development: Use modern AI coding tools such as Claude Code and Cursor as part of your daily practice, setting the standard for the rest of the Data Team
• Team Leadership: If filling the people-leader track, hire, coach, and grow a small team of AI engineers as the function scales
• Engineering Standards: Participate in code reviews, follow Git workflows and CI/CD practices, and contribute to evolving the team's AI engineering conventions
• Continuous Learning: Stay current with the rapidly evolving AI tooling landscape, bringing ideas back to the team and helping raise the bar over time
Qualifications
• Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field, or equivalent professional experience
• 5+ years of software engineering experience, with at least 2 years building AI or agentic systems used by a real organization
• Direct experience building agents, plug-ins, or AI applications used in a real working environment, not coursework, prototypes, or POCs
• Hands-on experience with modern agent tooling such as Claude Code, MCP (Model Context Protocol), LangGraph, Mastra, Pydantic AI, or comparable frameworks
• Strong programming skills in Python, including async patterns, type hints, and testing
• Working knowledge of large language model APIs (Anthropic, OpenAI, or comparable), with hands-on prompt engineering, evaluation, and iteration on production prompts
• Working knowledge of AI governance and security including prompt injection mitigation, data leakage prevention, model risk, and vendor review
• Track record of driving adoption of AI or technical solutions with users outside the immediate engineering team
• Strong written and verbal communication skills with the ability to explain technical AI concepts to non-technical stakeholders and to gather requirements from business users
• Comfortable with Git version control, code review, and modern engineering workflows including CI/CD
• Self-motivated with the ability to work independently in a remote environment while collaborating effectively across a distributed team
• Preferred: People leadership experience including managing 2 to 5 engineers and running technical hiring
• Preferred: Experience extending vendor AI platforms in the data and analytics ecosystem (Salesforce Einstein, Tableau Pulse-AI, BI vendors, vertical SaaS)
• Preferred: Mid-market or operational and manufacturing context, vs. a purely big-tech background
• Preferred: Familiarity with manufacturing systems such as Infor Syteline, ERP platforms, or shop floor and quality systems
Location
Fully remote within the United States. May require up to 10% travel to IEM facilities for team collaboration, project kickoffs, and stakeholder meetings.
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The AI Implementation Lead is the Data Team's hands-on builder, deploying AI solutions where Data Team work meets the business. The ideas are coming faster than the implementation; this role exists to close that gap. The work spans agents, plug-ins, applications, integrations, and whatever form the use case calls for. You will scale IEM's existing AI-native infrastructure (Claude Code in daily use, multiple local MCP servers wired to production data systems, an internal multi-agent portfolio across analytics and operations, and an established AI-assisted development culture) from a director-led prototype into a Data Team production platform. This role works in coordination with IEM's Enterprise AI team, which sets enterprise AI strategy and policy; the AI Implementation Lead applies that framework to the solutions you build rather than authoring it. This is the first dedicated AI engineer on the Data Team, offered with track flexibility as either a principal individual contributor or a hands-on people leader managing 2 to 4 engineers as the function scales.
Ideal Candidate Profile
You have 5+ years of software engineering experience with hands-on AI and agentic system development. You have built agents, plug-ins, or AI applications that real organizations have used, not coursework or POCs. You think first about the use case and the user, then about the system, then about the model. You write production code yourself and pride yourself on building solutions that actually get adopted. You partner naturally with non-technical stakeholders, translating fuzzy AI ideas into deployed solutions and surfacing the questions behind the questions. You are comfortable working with modern agent tooling such as Claude Code, MCP, LangGraph, Mastra, or Pydantic AI, and you treat AI-assisted development as a daily multiplier for engineering, governance, documentation, and adoption work. You have a working knowledge of AI governance and security including prompt injection mitigation, data leakage prevention, and model risk. You are excited about defining a new function inside a Data Team that already has the foundation in place.
Key Responsibilities
• AI Solution Deployment: Build agents, plug-ins, applications, and integrations as use cases emerge from the Data Team's work and the partners it supports
• Agent Development: Design and develop custom agents and agent harnesses, including MCP servers, orchestration logic, prompt engineering, and eval scaffolding. Agents are the centerpiece of the function.
• Vendor AI Extension: Build on top of Salesforce Einstein, Tableau Pulse-AI, ETQ-AI, Fellow.ai, and the data-platform AI capabilities IEM brings on next
• Solution Discovery: Run intake conversations with internal partners to surface high-value AI use cases, pressure-test feasibility against existing tooling, and prioritize the implementation roadmap
• AI Governance and Security: Build with data leakage prevention, prompt injection mitigation, and model risk as first-class concerns, in alignment with IEM's broader AI policy framework
• Adoption and Enablement: Train users, write documentation, and run enablement programs so the AI solutions you deliver get used by the people they were built for
• Cross-Functional Delivery: Coordinate with the internal teams the Data Team serves through build and rollout, keeping stakeholders aligned as solutions move from idea to deployment
• Documentation: Maintain design notes, integration docs, and runbooks for the AI solutions you deliver so the team can support and extend your work
• AI-Assisted Development: Use modern AI coding tools such as Claude Code and Cursor as part of your daily practice, setting the standard for the rest of the Data Team
• Team Leadership: If filling the people-leader track, hire, coach, and grow a small team of AI engineers as the function scales
• Engineering Standards: Participate in code reviews, follow Git workflows and CI/CD practices, and contribute to evolving the team's AI engineering conventions
• Continuous Learning: Stay current with the rapidly evolving AI tooling landscape, bringing ideas back to the team and helping raise the bar over time
Qualifications
• Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field, or equivalent professional experience
• 5+ years of software engineering experience, with at least 2 years building AI or agentic systems used by a real organization
• Direct experience building agents, plug-ins, or AI applications used in a real working environment, not coursework, prototypes, or POCs
• Hands-on experience with modern agent tooling such as Claude Code, MCP (Model Context Protocol), LangGraph, Mastra, Pydantic AI, or comparable frameworks
• Strong programming skills in Python, including async patterns, type hints, and testing
• Working knowledge of large language model APIs (Anthropic, OpenAI, or comparable), with hands-on prompt engineering, evaluation, and iteration on production prompts
• Working knowledge of AI governance and security including prompt injection mitigation, data leakage prevention, model risk, and vendor review
• Track record of driving adoption of AI or technical solutions with users outside the immediate engineering team
• Strong written and verbal communication skills with the ability to explain technical AI concepts to non-technical stakeholders and to gather requirements from business users
• Comfortable with Git version control, code review, and modern engineering workflows including CI/CD
• Self-motivated with the ability to work independently in a remote environment while collaborating effectively across a distributed team
• Preferred: People leadership experience including managing 2 to 5 engineers and running technical hiring
• Preferred: Experience extending vendor AI platforms in the data and analytics ecosystem (Salesforce Einstein, Tableau Pulse-AI, BI vendors, vertical SaaS)
• Preferred: Mid-market or operational and manufacturing context, vs. a purely big-tech background
• Preferred: Familiarity with manufacturing systems such as Infor Syteline, ERP platforms, or shop floor and quality systems
Location
Fully remote within the United States. May require up to 10% travel to IEM facilities for team collaboration, project kickoffs, and stakeholder meetings.
Apply tot his job
Apply To this Job