Requisition ID: 233208
Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.
1832 Asset Management L.P. is one of Canada’s largest assets managers and offers a comprehensive range of solutions through Dynamic Funds and ScotiaFunds, spanning every major sector, geographic region and investment discipline. The firm and its predecessor companies have been providing asset management services in Canada since 1877, as a manager of mutual funds and providing investment solutions for private clients, institutions, foundations, endowments and managed asset programs.
Today, the firm has over $100 billion in assets under management, and its experienced investment management team is active across capital markets and asset classes, deploying traditional and innovative strategies driven to deliver investment management excellence for our clients across our domestic and international businesses.
1832 Asset Management L.P. is a limited partnership, the general partner of which is wholly owned by Scotiabank.
As a Quantitative Developer Analyst and core member of our Quantamental & Derivatives (Q&D) group, you will collaborate with senior engineers and quants to design, build, and evolve the technology stack that transforms data and code into investable insights. This role is ideal for an engineer who enjoys hands-on coding, learning from experts, and contributing to mission-critical systems in a highly collaborative environment. You will work closely with quants, data scientists, and portfolio managers to deliver robust, scalable systems that power systematic investing across global markets.

WE’D ALSO LOVE TO WORK WITH YOU IF YOU HAVE THESE PREFERRED (BUT NOT REQUIRED) SKILLS & EXPERIENCE:

• Large Language Model (LLM) Experience: Fine-tuning, prompt engineering, or production deployment of GPT-4o/GPT-4o-mini, Gemini 1.5, Claude 3, Llama-3, or similar models.
• Retrieval-Augmented Generation (RAG): Design and implementation of RAG pipelines, including graphRAG architectures, embeddings, chunking strategies, vector stores (Pinecone, pgvector, Weaviate, Chroma, etc.), evaluation metrics, and guard-rail tooling.
• Model Context Protocol (MCP): Familiarity with orchestrating multi-step or multi-model LLM workflows using MCP standards.
• MLOps & LLM-Ops Tooling: Hands-on experience with LangChain, LlamaIndex, Ray Serve, BentoML, Feast, or comparable frameworks for scalable model serving and monitoring.
• Cloud & Data Engineering Expertise: Building and optimizing workloads on GCP (Vertex AI, Cloud Run, GKE) or equivalent, with emphasis on memory optimization and real-time/streaming data processing
  Location(s): Canada : Ontario : Toronto
  Scotiabank is a leading bank in the Americas. Guided by our purpose: “for every future”, we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.
  At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted

IS THIS ROLE RIGHT FOR YOU? IN THIS ROLE, YOU WILL:

Maintain and enhance the team’s current investment infrastructure:

• Build Full-Stack Solutions: Develop and maintain web applications, services, and APIs using Python (Django/FastAPI) and modern JavaScript/TypeScript frameworks (React, Next.js) in partnership with senior team members.
• Assist with Cloud Migration: Support the migration of on-prem workloads to Azure and other public clouds. Containerize services with Docker, define infrastructure as code (IaC) using Terraform, and configure Bitbucket Pipelines / Azure DevOps for automated deployments.
• DevOps & CI/CD – Contribute to automated testing, continuous integration, monitoring, and security hardening to uphold reliability and compliance.
• Data Engineering & Database Migration: Help design data models and migrate datasets across PostgreSQL, SQL Server, BigQuery, and time-series stores; ensure data quality and lineage.
• Quantitative Workflow Support – Productionize research prototypes, maintain back-testing pipelines, and troubleshoot live execution issues.
• Continuous Improvement – Follow coding standards, participate in peer reviews, and surface new tools that can enhance our research and trading platform.

DO YOU HAVE THE SKILLS THAT WILL ENABLE YOU TO SUCCEED IN THIS ROLE? WE’D LOVE TO WORK WITH YOU IF YOU HAVE:

• 5+ years of professional software engineering experience delivering production Python/Django applications.
• Hands-on experience deploying and operating cloud-native workloads on Azure (bonus: AWS or GCP); strong familiarity with Kubernetes, containerization, and IaC tooling such as Terraform.
• Practical knowledge of Bitbucket (or GitHub/GitLab) and building CI/CD automations with Bitbucket Pipelines and/or Azure DevOps.
• Demonstrated success executing database migrations and schema evolution across heterogeneous architectures (e.g., SQL Server ➜ PostgreSQL, on-prem ➜ cloud).
• Advanced SQL skills plus experience with NoSQL/time-series data stores (Redis, Influx, TimescaleDB).
• Solid understanding of distributed systems, concurrency, secure coding, and performance optimization.
• Exposure to data pipelines (ETL/ELT), event-driven architectures (Pub/Sub, Kafka), and financial or time-series data sets.
• Bachelor’s degree in Computer Science, Engineering, or a related field
• Clear, concise communication skills and a collaborative mindset.