Forward Deployed Engineer II

Kinaxis

. Own customer solutions end-to-end by rapidly prototyping, iterating, and deploying solutions in live operational environments .

Posted 10/22/2026full-timeRemote • United States, CanadaJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building LLM-based applications and multi-agent workflows while effectively managing customer solutions and relationships. Proficient in backend programming, data engineering, and leveraging modern AI-assisted development tools to drive business outcomes.

Highest-signal resume keywords

LLM-Based Application DevelopmentPython ProficiencyData Pipeline ManagementREST And Streaming APIsCustomer-Facing Technical Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software Engineering FundamentalsMulti-Agent WorkflowsPrompt And Context EngineeringData ModelingDebugging Real-World Data

Soft Skills

Customer Relationship ManagementMentoring And GuidanceCollaboration In Multidisciplinary Teams

Tools & Technologies

AirflowKafkaSnowflakeDatabricksClaude Code

Industry Keywords

AI-Assisted DevelopmentEnterprise-System IntegrationOperational EfficiencySLA ImprovementsAgentic Orchestration Patterns

Tech Stack

Tools & technologies

AirflowBigQueryKafkaPython

About the role

Key responsibilities & impact

Own customer solutions end-to-end by rapidly prototyping, iterating, and deploying solutions in live operational environments
Build trusted customer relationships from individual contributors to executive sponsors and serve as the technical face of Kinaxis
Manage multiple customer engagements in parallel based on customer priorities and pod capacity
Operate in a small, multidisciplinary, self-directed POD team
Partner with Product and Engineering to turn field learnings into durable platform capabilities
Mentor customer operators and guide teams through solution transitions and adoption
Measure success through business outcomes such as efficiency gains, exception reduction, and SLA improvements
Bring field learnings back into the platform and help create scalable capabilities

Requirements

What you’ll need

Hands-on experience building LLM-based applications, RAG systems, multi-agent workflows, tool-use patterns, prompt and context engineering
Familiarity with modern AI-assisted development tools, including Copilot, Claude Code, and agentic IDE workflows
Understanding of agentic orchestration patterns, including single- versus multi-agent architectures and handling memory and state
Solid, hands-on software engineering fundamentals and experience building and shipping real systems
Proficiency in at least one backend language; Python strongly preferred
Ability to read and contribute across a polyglot codebase
Experience with REST and streaming APIs, authentication patterns, and enterprise-system integration
Ability to build and maintain data pipelines for ingestion, transformation, serving, and monitoring using Airflow, Kafka, dbt, or equivalents
Working familiarity with modern data platforms such as Snowflake, Databricks, BigQuery, or similar
Data modeling and performance fundamentals
Strong debugging instincts with imperfect, real-world data in production environments
2–5 years of professional experience in software engineering, data engineering, solutions engineering, or a technical-founder role
Customer-facing technical experience is a strong signal, though not a requirement
Ability to travel as required, up to 50%

Benefits

Comp & perks

Flexible vacation and Kinaxis Days (company-wide days off)
Flexible work options
Physical and mental well-being programs
Regularly scheduled virtual fitness classes
Mentorship programs, training, and career development
Recognition programs and referral rewards
Hackathons