Forward Deployed Engineer

kadence; Austin (TX); Vor Ort; USD 150.000 - 230.000; Vollzeit

Zusammenfassung

Kadence Talent is partnering with a dynamic startup building the foundational intelligence layer enabling frontier AI systems to understand, reason, and act within complex enterprise environments.

The role requires leading end-to-end delivery, architecting advanced decision-making systems, and contributing to platform evolution. You will engage with customers, shape the product roadmap, document rigorously, and uphold engineering standards in a fast-paced setting.

Qualifikationen

• Extensive history delivering production-grade software within client or partner settings.
• Ability to drive complex initiatives from initial parameters to live execution.
• Strong background with SQL and Python in cloud data environments.
• Experience inspecting unfamiliar codebases, debugging stacks, and solving problems.

Aufgaben

• End-to-End Delivery: lead projects from discovery to production release.
• Application Engineering: build decision-making systems using ontologies, rules, graphs, and predictive models.
• Upstream Contributions: extend platform utilities and contribute back to core repositories.
• Roadmap Direction: translate deployment signals into actionable product feedback.
• Technical Discovery: run customer sessions, demonstrations, and feasibility proofs.
• Documentation Standards: keep docs synchronized with development and create scalable guides.
• Engineering Discipline: avoid unvetted work and ensure root-cause analysis.

Kenntnisse

SQL

Python

Cloud data architectures

Declarative/logic programming

Systems analysis

Stakeholder engagement

Jobbeschreibung

Kadence Talent are working with an exciting startup who are building the foundational intelligence layer that teaches frontier AI systems how to truly understand, reason through, and act upon the complex operational reality of the modern enterprise. This is an opportunity to join a growing organization, and help solve high-stakes challenges where intelligent automation meets large-scale business logic.

Core Responsibilities

• End-to-End Delivery: Take full accountability for total project lifecycles, steering initiatives from initial discovery through semantic modeling, coding, performance tuning, and production release without shifting tasks between teams.


• Application Engineering: Architect and deploy advanced decision-making systems utilizing enterprise ontologies, deterministic rules, graph analytics, optimization models, and predictive algorithms.


• Upstream Contributions: Resolve platform limitations by authoring missing utilities, examining codebase internals, framing analytical hypotheses, and submitting code alterations back to core repositories.


• Roadmap Direction: Translate empirical deployment signals, usage patterns, and operational bottlenecks into structured, actionable feedback for product development.


• Technical Discovery: Lead hands-on customer sessions, architectural demonstrations, and feasibility proofs to objectively determine solution viability.


• Documentation Standards: Maintain synchronized repository documentation concurrently with development work and convert bespoke implementations into scalable internal reference guides.


• Engineering Discipline: Uphold rigorous technical standards by avoiding unvetted configuration workarounds, undocumented environment drift, or hasty service reboots without root-cause analysis.

Qualifications

• Professional Background: Extensive history developing and releasing production-grade software applications, featuring direct engagement within client or partner settings.


• Project Lifecycle Mastery: Demonstrated capability to drive complex initiatives independently from vague initial parameters to stable live execution.


• Technical Proficiency: Advanced command of SQL and Python, accompanied by familiarity with cloud data architectures and declarative or logic-oriented programming languages.


• Systems Analysis: Proven aptitude for inspecting unfamiliar codebases, analyzing execution stack traces, and troubleshooting foreign technical environments.


• Stakeholder Engagement: Exceptional communication capabilities required to converse authoritatively with both executive business leaders and senior technical staff.


• Autonomous Execution: High resilience and effectiveness when operating within unstructured, fast-paced environments characterized by considerable enterprise friction.

Preferred Qualifications

• Prior success developing analytical, operational, or reasoning systems that achieved sustained, long-term production utilization.


• Academic or practical background in optimization frameworks, constraint logic programming, rule-based engines, graph theory, or structured machine learning.


• Functional expertise spanning semantic data modeling, pipeline architecture, and enterprise compliance governance.


• Established history of contributing foundational features, internal tooling, or generalized abstractions to shared codebases.


• Background working across enterprise software, artificial intelligence, or specialized data analytics platforms.