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.

What you'll do 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).

Develop and iterate on prompts, context strategies, and orchestration logic; establish version control and review practices for prompts as first-class artefacts.

Apply appropriate guardrails, input/output validation, and human-in-the-loop checkpoints for agents that take consequential actions.

Evaluate build-versus-buy trade-offs across model providers, frameworks, and managed services, and make pragmatic recommendations.

Retrieval-Augmented Generation & Knowledge Systems

Build and maintain RAG pipelines end to end: ingestion, parsing, chunking, embedding, indexing, retrieval, re-ranking, and grounded generation.

Select and operate vector stores appropriate to the workload (e.g. 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.

Design document processing workflows across heterogeneous and messy source formats, including incremental refresh and deletion handling.

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.

Manage infrastructure as code (e.g. Terraform) and keep environments reproducible.

Optimise for cost, latency, and throughput, including caching, batching, streaming responses, and right-sizing model selection to the task.

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.

Instrument applications with tracing and structured logging (e.g. OpenTelemetry, Cloud Trace, Langfuse, Arize Phoenix, or equivalent) to make agent behaviour debuggable.

Build monitoring and alerting for quality, latency, error rates, token consumption, and cost, and act on degradation proactively.

Track groundedness, hallucination, and refusal behaviour in production, and close the loop between observed failures and system improvements.

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; contribute to shared libraries and internal tooling.

Work within Git-based collaborative development practices, including branching strategies, pull requests, and code review.

Apply sound API design, dependency management, and packaging practices to AI services.

Balance rapid prototyping with the discipline required to make prototypes production-ready.

Documentation & Enablement

Maintain explicit 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.

Support colleagues and clients in adopting the systems you build.

What you'll need to bring

Proven experience building and deploying production applicat

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