Full Stack Developer (GWO)

Scotiabank•Toronto, ON

1d•Onsite

About The Position

We are looking for a Software Developer (Level 6) to join our Analytics & AI team and help build modern, secure, and scalable applications that bring data, automation, and AI-enabled workflows to life. In this role, you will contribute across the full technology stack—designing user-friendly interfaces, developing backend services and APIs, integrating data sources, and supporting AI-powered solutions such as Retrieval-Augmented Generation (RAG), large language model integrations, and agentic workflow frameworks including LangChain and LangGraph. You will work with engineering, data science, analytics, and business partners to deliver production-ready solutions within a regulated enterprise environment where security, reliability, and thoughtful design are essential.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, or a related technical discipline; equivalent hands-on software development experience may be considered.
1–3 years of software development experience, including internship, co-op, academic project, or professional experience building applications.
Proficiency in at least one programming language such as Python, JavaScript/TypeScript, or Java.
Hands-on experience with frontend development using React, Angular, Vue, or a comparable framework.
Hands-on experience with backend or API development using RESTful services, microservices, or comparable service-based architecture.
Hands-on experience with Git-based version control and common branching, pull request, and code review workflows.
Exposure to large language models and AI application development through academic, project, internship, or professional experience.
Experience or familiarity with agentic AI concepts such as tool use, workflow orchestration, memory, planning, and state management.
Experience or familiarity with LLM APIs or platforms such as OpenAI, Azure OpenAI, Anthropic, or similar services.
Working knowledge of prompt engineering techniques and structured outputs.
Working knowledge of RAG architectures, including ingestion, chunking, embeddings, retrieval, and response generation.
Working knowledge of vector databases such as FAISS, Pinecone, Chroma, Weaviate, pgvector, or similar technologies.

Nice To Haves

Cloud platform experience, preferably Microsoft Azure; experience with AWS or Google Cloud may also be relevant.
Docker or other containerization technologies.
CI/CD pipeline concepts and tools for automated build, test, and deployment workflows.
MLOps or model lifecycle concepts, including deployment, monitoring, evaluation, or versioning.
Software development experience in a regulated, financial services, or enterprise technology environment.
Experience or familiarity with LangChain, LangGraph, or comparable LLM orchestration frameworks.

Responsibilities

Develop and maintain full-stack web applications, including frontend experiences and backend services.
Build responsive, accessible user interfaces using modern frameworks such as React, Angular, Vue, or similar technologies.
Design, develop, and support backend services and APIs using languages such as Python, Node.js, or Java.
Work with relational and non-relational databases, including SQL and NoSQL technologies.
Contribute to AI-powered applications and tools, including Retrieval-Augmented Generation (RAG) solutions for knowledge retrieval and response generation, workflow orchestration using LangChain, LangGraph, or comparable frameworks, prompt engineering, structured outputs, and large language model integrations.
Support agent-based systems that automate repeatable tasks, connect to tools and data sources, and improve decision support.
Integrate applications with internal and external APIs, databases, and enterprise data sources.
Support data transformation, ETL processes, validation workflows, and data quality checks.
Work with structured and semi-structured datasets used in analytics and AI-enabled applications.
Write clean, maintainable, well-tested code that can be supported in production.
Participate in code reviews, unit testing, integration testing, and release readiness activities.
Apply secure coding practices, privacy considerations, and enterprise governance standards throughout the development lifecycle.
Work with partners across analytics, data science, operations, and technology to understand needs and deliver practical solutions.
Translate business requirements into technical designs, user stories, and working application features.
Contribute to agile delivery, sprint planning, backlog refinement, and regular deployment cycles.