Forward Deployed Engineer I

As a Forward Deployed Engineer at Kinaxis, you will design, build, and deploy Agentic AI solutions for customers on the Maestro Platform. You work closely with customers across the organization, delivering end-to-end solutions in fast-paced environments. You shape reusable patterns from live deployments and own outcomes from start to finish, driving real business value. This role combines startup-like ownership with enterprise-scale impact, within a multi-disciplinary pod focused on customer success.

• Flexible vacation and Kinaxis Days
• Flexible work options
• Well-being programs
• Mentorship and career development
• Hackathons
• Recognition programs and referral rewards

• Own customer solutions end-to-end from prototyping to live deployment
• Deliver relentless customer value and build relationships from IC to executive sponsor
• Run multiple customer engagements in parallel based on priorities and pod capacity
• Operate as a small, tight, multi-disciplinary unit with rapid task switching
• Collaborate with Product and Engineering to convert field learnings into durable platform capabilities
• Mentor customer operators and guide teams through transitions to new ways of working
• Drive adoption and lead organizational change at customer sites
• Own the outcome with high agency and minimal close supervision

• 2–5 years of professional experience in software/data/solutions engineering or a technical founder role
• Hands-on experience building LLM-based applications and RAG/multi-agent workflows
• Proficiency in Python (backend) and ability to work across a polyglot codebase
• Experience with REST and streaming APIs, authentication, and enterprise integrations
• Ability to build and maintain data pipelines (Airflow, Kafka, dbt) and work with data platforms (Snowflake, Databricks, BigQuery)
• Strong debugging skills in production environments
• Nice-to-have: exposure to supply chain planning, logistics, or operations

• strong communication across engineering, product, and business stakeholders
• high ownership and bias to action
• curious and pragmatic approach to new tools
• LLM-based application development
• RAG systems and multi-agent workflows
• prompt and context engineering