Forward Deployed Engineer

Kinaxis Inc.•Remote,

11d•Remote

About The Position

Kinaxis is building a Forward Deployed Engineers team whose singular mission is to help customers transform how work gets done in the Agentic Age. This is not a consulting role and it is not a features team. It is a multi-disciplined deployment unit operating with the speed and ownership culture of a startup. You will work shoulder-to-shoulder with customers — from warehouse floor to executive sponsor — designing, building, and shipping Agentic AI solutions on the Kinaxis Maestro Platform. When a solution works, you abstract it into a reusable pattern. When something breaks in production, you own the fix. You operate in a POD team: tight, multi-disciplinary, and self-directed. We are scrappy, resourceful, and self-directed. We move fast, trade tasks to whoever is closest to the need, and stay relentlessly focused on customer impact. Attitude is the biggest asset. We hire for trajectory and mindset, not years on a resume. The right bias to action, ownership, and appetite to keep learning cannot be taught — but every technical skill here can be. Bring those, and we will invest in the rest.

Requirements

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 (Copilot, Claude Code, agentic IDE workflows) as daily practice, not novelty.
Understanding of agentic orchestration patterns — when to use single vs. multi-agent architectures, how to handle memory and state.
Solid, hands-on software engineering fundamentals: you have built and shipped real systems, not just scripts.
Proficiency in at least one backend language (Python strongly preferred) and the ability to read and contribute across a polyglot codebase.
Experience with REST and streaming APIs, authentication patterns, and integration with enterprise systems.
Ability to build and maintain data pipelines — ingestion, transformation, serving, and monitoring — using tools such as Airflow, Kafka, dbt, or equivalents.
Working familiarity with modern data platforms (Snowflake, Databricks, BigQuery, or similar), including 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. Strong candidates at either end of that range are welcome to apply.

Nice To Haves

Prior exposure to supply chain planning, logistics, or operations domains is a genuine plus — not a requirement
Prior exposure to customer-facing technical roles — solutions engineering, forward deployment, technical consulting, or a startup where you wore multiple hats — is a strong signal, though not a requirement.

Responsibilities

Own customer solutions end-to-end. Rapidly prototype, iterate, and deploy solutions in live operational environments where the stakes are real and the timeline is short.
Deliver relentless customer value. Build trusted relationships from the IC level to the executive sponsor and become the technical face of our company in the room.
Run multiple threads. Carry several customer engagements in parallel based on customer priorities and pod capacity. Set expectations early, renegotiate honestly when reality shifts, operate without close supervision, and own the outcome.
Operate as a SEAL team. A small, tight, multi-disciplinary unit with focus and urgency — tasks are traded seamlessly to whoever is closest to the skills needed.
Make the product better. Partner with Product and Engineering to convert field learnings into durable platform capabilities — not one-off scripts.
Drive adoption and lead the change. Mentor customer operators and guide their teams through solution transitions, so the new way of working actually sticks.