About the role

Senior AI Software Engineer building agentic AI harnesses for Kinaxis’s Databricks-based enterprise data fabric. Enabling secure, governed workflows across GCP and Azure.

Responsibilities

Design and build the AI harness layer between LLMs, agents, Databricks, governed data products, and enterprise applications
Build reusable runtimes for tool calling, context assembly, planning, state management, retries, approvals, observability, evaluation, and fallback behavior
Connect agent workflows to Databricks, Delta Lake, Unity Catalog-style metadata, notebooks/jobs, SQL endpoints, cloud storage, and governed data products across GCP and Azure
Create safe agentic data workflow patterns for querying, transforming, summarizing, enriching, and acting on enterprise data while enforcing policy, provenance, lineage, and human-in-the-loop controls
Design isolation boundaries across data, compute, identity, metadata, tools, prompts, memory, network paths, secrets, and execution context for multi-tenant workloads
Deliver APIs, SDKs, libraries, reference implementations, templates, and operational runbooks
Support team members and cross-functional stakeholders in AI system initiatives

Strong software engineering background with experience building distributed systems, platform services, APIs, and production-grade frameworks
Hands-on experience with Databricks and/or modern cloud data platforms, preferably across GCP and Azure
Deep understanding of LLM integration patterns, agent orchestration, tool use, structured generation, retrieval, evaluation, and guardrails
Practical knowledge of Data Fabric concepts, metadata, lineage, governance, semantic models, and enterprise data access patterns
Experience designing secure multi-tenant systems with strong isolation across data, compute, identity, and runtime execution
Ability to set architectural direction, write code, prove patterns, and turn prototypes into durable platform capabilities