At Hygiena we believe:

In providing the highest quality products & service Being a leader in innovation Having a compelling desire to improve and win in the marketplace In contributing positively not only in the workplace, but in our community and environment With rapid growth comes opportunity. We are looking for an Applied AI Engineer (LLM Applications Engineer) to join our team onsite in our Mississauga, Ontario Canada office This is a full-time / exempt position.

Responsibilities:

Design, build, and ship production-grade GenAI/LLM applications across Hygiena products including SureTrend — copilots, conversational agents and assistants, workflow automation, and decision support. Implement agentic workflows (tool use, multi-step execution, human-in-the-loop controls) with attention to reliability, safety, and explicit failure modes. Design and build advanced retrieval and knowledge systems (RAG): hybrid search, vector and graph stores, indexing strategies, reranking, caching, and source attribution — grounding generation in authoritative sources such as regulatory frameworks (FSMA, HACCP, Codex), validation standards (AOAC, MicroVal, AFNOR), SOPs, and technical documentation. Engineer for production across the full SDLC (build u2192 test u2192 deploy u2192 monitor u2192 iterate). Implement MLOps/GenAIOps practices (CI/CD, reproducibility, environment parity, model/prompt/agent versioning) and build evaluation and observability for GenAI and agentic systems — tracing and instrumentation, regression test suites, automated scoring, and prompt/policy iteration loops. Security-test and red-team LLM and agentic systems — probing for prompt injection, jailbreaks, data/PII leakage, insecure tool use,



and unsafe or non-compliant outputs; build adversarial test suites and harden systems and guardrails based on findings. Design for secure deployment with access controls, auditability, sensitive-data handling, and responsible-AI guardrails suited to a regulated domain. Configure and integrate AI solutions into enterprise and customer environments — APIs, data systems, and business applications (e.g., LIMS, QMS, ERP, and collaboration tools) — balancing quality, latency, cost, privacy, and adoption. Support data preparation, transformation, and validation that feeds retrieval, evaluation, and model training/deployment. Collaborate with Product Managers, Data Engineers, ML Engineering, and food safety scientists; translate ambiguous product and domain needs into clear technical requirements and tradeoffs; document patterns, decisions, and instrumentation; and stay current on emerging AI tools, models, and frameworks, bringing promising ones into real Hygiena use cases.

Qualifications:

Bachelor’s Degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience; Master’s a plus. Minimum of 3–5 years of professional software, AI, or ML engineering experience, with strong backend engineering fundamentals. 1+ years of handsu2011on experience building and deploying LLMu2011powered applications in production, with real delivery considerations (latency, cost, reliability, security). Strong proficiency in Python and experience building APIs/services (e.g.,



REST/gRPC) and integrating with enterprise systems. Handsu2011on experience with at least one frontier GenAI platform — Anthropic, Google, or OpenAI — and its key building blocks (e.g., Claude API, Claude for Enterprise, tool use, extended thinking, Claude Code; Gemini API, Vertex AI Agent Builder, grounding; OpenAI Assistants/Responses APIs, Agents SDK). Experience building advanced retrieval/search systems (hybrid retrieval, vector search, reranking) and comfort working across multiple data stores (vector, graph, relational/document/search). Experience implementing agentic patterns (context management, memory/state, orchestration, and tool use). Familiarity with conversational AI frameworks (LLMs, chatbots, virtual assistants, agents) and their APIs. Experience with MLOps/LLMOps practices (evaluation frameworks, model monitoring, prompt management) on a platform such as Azure Machine Learning, and with LLM application frameworks/tooling (e.g., LangChain, LlamaIndex), preferred. Experience securityu2011testing or redu2011teaming LLM/agentic systems (e.g., prompt injection, jailbreaks, data leakage), and familiarity with frameworks such as the OWASP Topxa0 10 for LLM Applications, preferred. Experience with cloud environments (AWS, Azure, and/or Google Cloud), common platform services (storage, compute, IAM, networking), containers, and CI/CD preferred. Data engineering or datau2011science background (e.g., Spark, Airflow/dbt, feature engineering, experimentation, model evaluation), and experience in a regulated or safetyu2011critical industry such as food safety, diagnostics, or life sciences, is advantageous but not required.

Skills:

Strong product sense and a delivery mindset — take

📌 Applied AI Engineer (Mississauga)
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