Job Description & Details
This is a heavy-hitting AI Solution Architect role sitting at the intersection of enterprise consulting and high-stakes financial services innovation. You will be building out production-grade machine learning pipelines, document intelligence systems, and multi-agent frameworks specifically tailored for RBC. If you enjoy bridging the gap between deep technical research and scalable, business-critical cloud deployments, this one is worth your attention.
What You'll Actually Be Doing
Your days will be split between high-level architectural design and getting your hands dirty with cutting-edge AI engineering. You'll lead R&D efforts focusing on RAG implementations, complex PDF parsing, and automated decisioning platforms. Expect to spend a lot of time defining how models move from local Jupyter notebooks into resilient, monitored production environments on AWS and Azure, all while unblocking your engineering team and syncing up with non-technical stakeholders to prove out real ROI.
The Core Tech Stack
You need absolute fluency in Python and deep, production-level familiarity with modern AI frameworks like PyTorch, HuggingFace Transformers, and LangChain. Because you'll be building end-to-end systems, strong backend chops using FastAPI or Django paired with relational data stores like PostgreSQL are non-negotiable. They are specifically looking for someone who doesn't just know how to call an LLM API, but understands the underlying system design required to make distributed AI architectures hum reliably in a heavily regulated financial environment.
Interview Expectations
Expect the hiring team to grill you on system design under scale, specifically asking how you would architect a low-latency RAG pipeline that handles messy financial documents securely. They are secretly looking for your pragmatic trade-offs between cost, accuracy, and model drift in production. You will also likely face behavioral and technical scenarios testing your ability to mentor junior engineers through a failed model deployment without breaking delivery timelines.
Application Advice
Skip the generic project summaries and make sure your resume immediately highlights production deployments involving LangChain, vector databases, and heavy data processing pipelines. Weave in metrics related to model optimization, latency reduction, and cross-functional leadership, making sure keywords like 'Retrieval-Augmented Generation', 'FastAPI', and 'PyTorch' jump off the page to clear the ATS on your first pass.