Forward Deployed Engineer | IFS Copperleaf

IFS

9d•Hybrid

About The Position

The Forward Deployed Engineer works across Sales, Product, R&D, and Implementation to help win strategic customer deals and bring validated customer solutions into production. The role quickly deploys the current product using customer data to demonstrate value, and uses modern AI tools to prototype solutions when existing product capabilities do not meet a customer need. The engineer owns technical outcomes from discovery through production adoption, connecting customer requirements and technical work to measurable business value. The role combines customer discovery, technical scoping, architecture, implementation, evaluation, productionization, adoption, and handoff. Prototypes should test a value hypothesis quickly and safely within a deal’s timeline; production solutions should be engineered for security, reliability, observability, maintainability, and integration with customer architecture. The engineer measures adoption, workflow impact, reliability, and time-to-value, iterates with technical and business stakeholders, and works with Product and R&D to turn validated patterns into roadmap investments, reusable components, and platform improvements. The position supports asset-intensive organizations such as utilities, energy, water, and transportation. Success is measured by influenced ACV, time to a working prototype using customer data, the rate at which prototypes reach production, reuse of shipped patterns and accelerators, and customer gaps brought to Product and its decisions.

Requirements

Ability to lead technical discovery and value conversations with technical and business stakeholders, linking solutions to customer outcomes and deal value.
Ability to generalize customer-specific work into reusable accelerators.
Experience or capability in LLM application development, RAG, prompt and context engineering, and agent orchestration, including choosing between single- and multi-agent patterns and handling memory and state.
Use of modern AI coding tools such as Claude Code as part of regular work.
Familiarity with enterprise integrations in regulated utility markets, including systems such as ESRI ArcGIS/Smallworld, IBM Maximo/EAM, SAP/Oracle ERP/PPM, and PowerBI/reporting systems.
Technical and functional understanding of REST and streaming APIs, authentication patterns, and integration with enterprise systems of record.
Willingness to travel to customer sites as needed, up to approximately 50%.

Nice To Haves

Domain experience in asset-intensive industries such as utilities, energy, or transportation, or in capital and asset investment planning.
Software or data engineering background, including having built and shipped working software and writing maintainable code.
Fluency in Angular, Python, or a similar language, and comfort working in unfamiliar languages and codebases.
Understanding of SQL, data modeling, and pipelines for ingestion, transformation, and serving.
Strong debugging skills with imperfect customer data in live environments.
Production engineering depth in observability, performance, and operating at scale.
Prior customer-facing technical experience in solutions engineering, forward deployment, technical consulting, or a startup.
Prior experience deploying AI in enterprise or regulated environments.

Responsibilities

Work with Solutions Consultants and Account Executives on strategic accounts and important new business opportunities.
Own discovery, technical scoping, architecture, implementation, evaluation, productionization, adoption, and handoff for strategic customer solutions.
Deploy the current product using customer data and demonstrate customer value.
Build the smallest safe prototype needed to test a hypothesis within the deal timeline, then deliberately productionize solutions intended for production use.
Engineer production solutions for security, reliability, observability, maintainability, and integration with customer architecture.
Measure adoption, workflow impact, reliability, and time-to-value; iterate with technical and business stakeholders.
Partner with Product and R&D to turn validated customer patterns and production evidence into roadmap investments, reusable components, and platform improvements.
Turn recurring customer needs into reusable accelerators.
Develop and continuously update solution specifications with customers to inform Product and Engineering.
Work with Product to assess business potential, broader market needs, competitive differentiation, and functional and technical specifications as outcomes emerge.