We are seeking a highly skilled Cloud AI Engineer to design, build, and operate the AI systems and agentic applications that power our products and client engagements. You will leverage your expertise in Google Cloud Platform, Python, and modern LLM frameworks to take generative AI solutions from prototype to production — building retrieval systems, autonomous agents, and the evaluation and monitoring infrastructure that keeps them reliable. Your work will directly shape how our organisation and our clients put AI into the hands of real users.

Agentic AI & LLM Application Development Design, build, and deploy production-grade AI agents and multi-agent workflows using frameworks such as Google's Agent Development Kit (ADK), LangChain, or equivalent. Implement tool use, function calling, and structured outputs, integrating agents with internal APIs, databases, and third-party services (including via the Model Context Protocol). establish version control and review practices for prompts as first-class artefacts.

Build and maintain RAG pipelines end to end: ingestion, parsing, chunking, embedding, indexing, retrieval, re-ranking, and grounded generation. Vertex AI Vector Search, pgvector on Cloud SQL or AlloyDB, or equivalent). Implement hybrid and metadata-filtered retrieval strategies, and tune them against measured retrieval quality rather than intuition.

Build, containerise, and deploy AI services and APIs on Google Cloud Platform, primarily using Cloud Run, Cloud Functions, and Vertex AI. Design and manage supporting cloud infrastructure — Cloud SQL, BigQuery, Cloud Storage, Pub/Sub, Artifact Registry, Secret Manager — with sound security choices around networking, IAM, and service accounts.



Build and maintain the data pipelines that feed AI systems, spanning batch and streaming ingestion from APIs, databases, and file sources.

Write and optimise SQL against BigQuery and relational databases for both application queries and analytical workloads. Model and manage the operational data layer (Cloud SQL, Firestore, or similar) supporting agent state, session history, and application data. Implement data quality checks, schema management, and lineage where it materially affects downstream AI behaviour.

Evaluation, Monitoring & Observability Design and implement evaluation frameworks for LLM and agent systems, including golden datasets, offline eval suites, LLM-as-judge scoring, and regression testing across prompt and model changes. Build monitoring and alerting for quality, latency, error rates, token consumption, and cost, and act on degradation proactively. Investigate and troubleshoot AI-related incidents in production, including non-deterministic and hard-to-reproduce failures.

Python & Software Engineering Practice Write clean, tested, maintainable Python; Apply sound API design, dependency management, and packaging practices to AI services. Maintain clear documentation for AI systems, architectures, evaluation results, and operational runbooks. Communicate capabilities, limitations, and risks of AI systems honestly to technical and non-technical stakeholders.





Proven experience building and deploying production applications on Google Cloud Platform, particularly Cloud Run, Cloud SQL, and Vertex AI.

Strong

Python proficiency, with demonstrable experience building services and applications. Hands‑on experience designing and shipping LLM-powered systems, including at least one production RAG or agentic application. Solid SQL skills, including experience with BigQuery at scale.

Proficiency with version control systems and collaborative Git-based development practices. Demonstrated experience evaluating and monitoring AI systems in production, beyond manual spot‑checking. Direct experience with Google's Agent Development Kit (ADK) and Vertex AI Agent Engine.

Professional Machine Learning

Engineer, Professional Cloud Developer, or Qualified Data Engineer).

Experience with the Model Context Protocol (MCP) and building or consuming MCP servers.

Experience with LLM observability and evaluation tooling (Langfuse, Arize Phoenix, Weights & Biases, Vertex AI Evaluation, or equivalent). Frontend or full‑stack experience sufficient to build usable interfaces over AI services.

Experience with traditional ML and statistical modelling, and comfort collaborating with data scientists on hybrid systems. Familiarity with data visualisation tools (e.g. Looker, Data Studio, PowerBI).

Strong problem‑solving and analytical skills, with sound judgement about which problems warrant AI and which do not. Excellent communication and collaboration skills, including with non‑technical stakeholders. Security‑ and privacy‑conscious approach to handling data in AI systems.

Competitive salary and benefits package.

📌 Cloud AI Engineer (Vancouver)
🏢 Noise Digital
📍 Vancouver