Forward Deployed Engineer II - CAN

Kinaxis Inc.•Remote,

5d•Remote

About The Position

Kinaxis is hiring a Forward Deployed Engineer II for its customer-focused, multidisciplinary Forward Deployed Engineers team. The team builds and deploys Agentic AI solutions on the Kinaxis Maestro Platform, working directly with customers across operational and executive levels. This is a remote position, with the employee based anywhere in Canada or the United States, and travel as required up to 50%. The role is an existing vacancy. The engineer owns customer solutions from prototyping through production deployment, works across multiple customer engagements, and takes responsibility for fixes when production solutions break. The role emphasizes rapid delivery of customer value, clear communication and expectation-setting, and independent ownership in a self-directed POD team. The engineer also partners with Product and Engineering to turn field learnings into reusable platform capabilities, and helps customers adopt new solutions by mentoring operators and guiding teams through change. The role calls for hands-on experience building LLM-based applications and agentic workflows, solid software engineering skills, and the ability to build and maintain data pipelines and work with modern data platforms. Success is measured by customer and business outcomes, such as efficiency gains, exception reduction, and SLA improvements, rather than code volume. Kinaxis values initiative, adaptability, curiosity, clear communication, and a willingness to keep learning.

Requirements

2–5 years of professional experience in software engineering, data engineering, solutions engineering, or a technical-founder role.
Hands-on experience building LLM-based applications, RAG systems, multi-agent workflows, tool-use patterns, and prompt and context engineering.
Familiarity with modern AI-assisted development tools such as Copilot and Claude Code, and agentic IDE workflows, as part of daily practice.
Understanding of agentic orchestration patterns, including when to use single-agent versus multi-agent architectures and how to handle memory and state.
Solid, hands-on software engineering fundamentals, including experience building and shipping real systems.
Proficiency in at least one backend programming language; Python is strongly preferred.
Ability to read and contribute across a polyglot codebase.
Experience with REST and streaming APIs, authentication patterns, and enterprise-system integrations.
Ability to build and maintain data pipelines for ingestion, transformation, serving, and monitoring, using tools such as Airflow, Kafka, dbt, or equivalents.
Working familiarity with modern data platforms such as Snowflake, Databricks, BigQuery, or similar, including data modeling and performance fundamentals.
Strong debugging instincts when working with imperfect, real-world data in production environments.
Ability to communicate clearly across engineering, product, and business stakeholders, in writing, verbally, and in person.
Ability to travel as required, up to 50%.

Nice To Haves

Prior exposure to supply chain planning, logistics, or operations domains.
Prior exposure to customer-facing technical roles such as solutions engineering, forward deployment, or technical consulting, or experience at a startup wearing multiple hats.

Responsibilities

Own customer solutions end-to-end, rapidly prototyping, iterating, and deploying them in live operational environments.
Build trusted customer relationships from individual contributors to executive sponsors and serve as the company's technical representative in customer settings.
Manage several customer engagements in parallel, set expectations, renegotiate when circumstances change, and own outcomes without close supervision.
Work as part of a small, multidisciplinary, self-directed POD team, shifting tasks to address the most immediate needs.
Partner with Product and Engineering to turn field learnings into durable platform capabilities rather than one-off scripts.
Mentor customer operators and guide teams through solution transitions to drive adoption and lasting changes in working practices.
Measure success through business outcomes such as efficiency gains, exception reduction, and SLA improvements.
Bring customer and field learnings back into the platform and help make successful approaches reusable and scalable.
Communicate clearly with engineering, product, and business stakeholders.
Travel as required, up to 50%.