Software Engineer, AI Systems

Accountabilities • Build, deploy, and operate production LLM pipelines that coordinate model calls, tools, graph queries, retrieval, quality gates, and specialized agent handoffs.

• Extend agent orchestration workflows that move enterprise incidents from evidence gathering through analysis, review, and organizational learning.

• Design grounding and retrieval systems using graph traversal, vector search, and hybrid retrieval to provide models with relevant evidence and organizational knowledge.

• Develop robust evaluation frameworks, including representative datasets, scoring systems, regression suites, model comparisons, and human-review workflows.

• Implement production AI operations capabilities covering tracing, tool-call auditing, cost and latency monitoring, failure handling, and quality dashboards.

• Identify and address hallucinations, workflow loops, silent quality degradation, and other production issues before they affect customers.

• Evaluate and select AI models across major providers, balancing quality, latency, cost, context limitations, and operational risk.

• Partner with product and knowledge engineering teams to shape technical direction, architecture, and the broader AI roadmap.

• Contribute to secure enterprise AI practices, including tenant isolation, access controls, context protection, and safeguards against prompt injection.

Requirements

• Experience in AI or ML engineering with a track record of shipping production LLM systems used by real users.

• Hands-on experience developing and debugging multi-step, tool-calling or agentic workflows using LangGraph, LangChain, or an equivalent framework.

• Strong understanding of LLM evaluation, including representative datasets, regression testing, LLM-as-judge approaches, and/or human evaluation loops.

• Practical experience designing retrieval and context-assembly strategies, with a clear understanding of what information should be retrieved, how much context is appropriate, and why.

• Proven ownership of production systems through deployment, monitoring, incident response, and ongoing improvement, including experience diagnosing and resolving failures or regressions.

• Ability to work across multiple model providers and make informed trade-offs involving quality, latency, cost, context, and operational risk.

• Experience with Neo4j and Cypher or a comparable graph database, with the ability to understand and contribute to graph data modeling.

• Strong Python skills and production experience with technologies such as FastAPI, asynchronous services, automated testing, observability, and maintainable software interfaces.

• Experience with graph technologies such as Cypher, schema evolution, MERGE patterns, embeddings, or operating production knowledge graphs is an asset.

• Familiarity with enterprise AI security concepts, including prompt-injection mitigation, context-leak prevention, tenant isolation, role-based access, and policy-layer separation is valuable.

• Experience with Azure, hybrid search, Azure AI Search, Pinecone, MongoDB Atlas, pgvector, Elasticsearch, or similar technologies is an advantage.

• Previous experience in B2B enterprise SaaS environments is strongly preferred.

• Comfortable working autonomously in a small, fast-moving team and taking ownership across multiple areas of the technology stack.

• Strong communication and collaboration skills, with the ability to work effectively with technical, product, and domain specialists.

• Sponsorship is not provided for this position.

Benefits

• Remote work opportunity in Canada.

• High-ownership environment with direct collaboration with technical and product leadership.

• Opportunity to work on advanced LLM, agentic AI, retrieval, and knowledge-graph systems.

• Meaningful technical challenges in safety-critical enterprise applications where precision and traceability are central.

• Direct exposure to customer feedback and the opportunity to see engineering work reach production quickly.

• Opportunity to influence architecture, model selection, evaluation methodology, engineering practices, and the AI product roadmap.

• Work closely with a small team across AI engineering, product, and knowledge engineering.

• Exposure to enterprise customers across energy, utilities, infrastructure, construction, and manufacturing.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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