Software Engineer, Data & AI Applications

Fortive

4d•Remote

About The Position

We're building a modern data and AI platform that helps employees find trusted information, automate business processes, and make better decisions faster. As a Software Engineer, Data & AI Applications, you'll play a key role in shaping how enterprise data is transformed into intelligent, actionable experiences. You'll work across the full technology stack, from data ingestion and modeling to AI-powered applications and agent-driven workflows. This is an opportunity to work at the intersection of software engineering, data platforms, and applied AI while helping define the next generation of internal business tools. If you enjoy solving complex technical problems, building scalable systems, and experimenting with emerging AI technologies in a production environment, we'd love to hear from you.

Requirements

Experience in software engineering, data engineering, analytics engineering, or a related technical discipline.
Strong SQL skills and proficiency in at least one programming language, preferably Python.
Experience building and supporting cloud-based data warehouses and production data pipelines.
Hands-on experience with Snowflake and dbt.
Experience with data integration or ingestion tools such as Airbyte.
Experience developing applications that use large language models (LLMs), retrieval-augmented generation (RAG), tool calling, or agent-based workflows.
Familiarity with AI-assisted software development tools such as Claude Code, GitHub Copilot, Cursor, Codex, or similar platforms.
Understanding of APIs, source control, automated testing, CI/CD, and modern software engineering practices.
Strong problem-solving skills with the ability to troubleshoot across interconnected systems.
Effective communication skills and the ability to collaborate with both technical and business stakeholders.

Nice To Haves

Experience with Snowflake Cortex or other enterprise AI/LLM platforms.
Experience designing semantic layers, metadata systems, or governed retrieval architectures.
Familiarity with LLM evaluation frameworks, observability tools, token-cost optimization, and response quality monitoring.
Experience implementing role-based access controls, data governance standards, and data privacy protections.
Experience supporting internal applications from early-stage prototypes through enterprise-scale production adoption.

Responsibilities

Build and maintain reliable data pipelines using tools such as Airbyte, Snowflake, and dbt.
Design and optimize scalable data models that support analytics, reporting, search, and AI-powered applications.
Develop and maintain dbt models, testing frameworks, documentation, and deployment processes.
Ensure data quality, reliability, governance, and operational excellence across the data platform.
Build and enhance AI-powered applications that securely retrieve, interpret, and summarize business information.
Design retrieval, context-management, prompt-engineering, and tool-calling approaches that improve accuracy, transparency, and user trust.
Develop agent-based workflows that can complete multi-step business processes using approved tools and systems.
Create APIs, services, and integrations that connect AI applications with internal platforms and business systems.
Monitor system health, application performance, data quality, AI usage, and operational costs.
Troubleshoot issues across data pipelines, warehouse transformations, software services, and AI workflows.
Apply security, access control, testing, logging, and governance best practices throughout the development lifecycle.
Partner with business stakeholders to translate requirements into scalable and maintainable solutions.
Leverage AI-assisted development tools to improve productivity while maintaining high standards for code quality, testing, and documentation.