AI, Enablement Engineer

Fortive•Minneapolis, MN

About The Position

The AI Enablement Engineer leads the delivery and scaling of AI-powered automation solutions across multiple internal business units. This is a technical and strategic role focused on embedding AI into real-world operational workflows—driving measurable productivity, quality, and efficiency improvements across the enterprise. This role partners closely with internal stakeholders to understand business needs, rapidly builds and deploys AI-enabled solutions, and establishes repeatable patterns that other teams can adopt. The work of this role will span engineering, product, operations, and business domains, creating the “flywheel” that accelerates AI adoption across departments. Once solutions are established and teams are enabled, this colleague will transition to new problem areas and help replicate success globally. This role is ideal for a software or systems engineer with deep technical acumen, strong business intuition, and excellent communication skills—including experience collaborating with global and offshore teams.

Requirements

Bachelor’s or master’s degree in Computer Science, Data Science, or related field.
5-8 years of experience in software engineering, systems engineering, or automation development, preferably across multiple domains.
Demonstrated experience building and deploying AI/ML or automation solutions using tools such as Python, cloud services (AWS/GCP/Azure), REST APIs, orchestration platforms, or GenAI APIs (e.g. OpenAI, Bedrock, Azure OpenAI).
Strong communication and stakeholder engagement skills; able to translate business needs into technical requirements and vice versa.
Proven success working across cross-functional and globally distributed teams, including coordination with offshore partners or delivery centers.
Ability to take ownership of the entire AI solution lifecycle—from problem framing to deployment to handoff.
Familiarity with enterprise SDLC, DevOps practices, security, and compliance constraints in complex environments.
Self-sufficient and self-starter who is able to apply solid thought processes to breakdown problems and complex situations into workstreams/actions that will drive impact and value.
Able to quickly learn and master FBS tools.
Demonstrated proficiency in time and project management, as well as ability to handle priority setting and ability to resolve prioritization conflicts
Ability to travel 25-30% is required.

Nice To Haves

Experience designing intelligent automation in business process areas (e.g. document processing, ticket triage, reporting workflows).
Knowledge of prompt engineering, agent frameworks, or LLM orchestration patterns is a strong plus.
Familiarity with MLOps or model lifecycle management tools is helpful but not required.
Experience working in organizations with federated operating models or shared services.

Responsibilities

Act as the technical owner and developer of AI-powered automation projects—from discovery through delivery—across internal business domains (e.g. finance, operations, HR, customer support, product development).
Translate ambiguous business problems into concrete AI/ML opportunities and deliver working solutions using tools like large language models, intelligent agents, workflow automation, and custom software integrations.
Build prototypes and minimum viable solutions (MVS), demonstrate ROI, and work with engineering or ops teams to scale or productionize.
Engage directly with business stakeholders, SMEs, and process owners to identify high-impact opportunities for AI enablement.
Collaborate with global engineering, data, and automation teams to deliver and support deployed solutions—often working with offshore counterparts.
Serve as a technical liaison across functions, ensuring solutions meet business needs, compliance standards, and technical best practices.
Create enablement artifacts (templates, documentation, reusable code, training resources) to allow teams to self-sustain and expand AI usage after the initial implementation.
Provide mentorship and onboarding support to internal teams inheriting automation frameworks or tools.
Design playbooks and re-usable modules that accelerate repeat adoption across other business units.
Establish a scalable model for rolling out AI automation solutions across business centers: build → enable → transition → replicate.
Drive consistent alignment to enterprise standards for responsible AI usage, data access, and compliance.
Influence tooling and platform decisions to ensure long-term sustainability and extensibility of AI-driven workflows.