LocationBrookfield Place - 181 Bay Street
Technology ServicesTechnology Services (TS) is responsible for delivering all enterprise infrastructure, applications and related end user technology services across all Brookfield business groups.
Job DescriptionThe Senior AI Cloud Developer Analyst within Brookfield's Technical Service Group (TSG) builds and delivers AI solutions from prototype to production, implementing scalable, secure and reliable AI capabilities that meet business needs across the Brookfield environment. This role translates early-stage concepts and prototypes into production-ready systems, working closely with cross-functional technology teams.
Key Responsibilities- Implement and scale AI capabilities across projects using AWS & Azure native services, building and maintaining the appropriate AI infrastructure and security controls required to run AI applications reliably in the production environment.
- Translate prototype and concepts into production-ready implementations by building modular services, APIs, data flows and integration patterns following and applying reusable patterns and enterprise standards.
- Implement AI features using LLMs, agent frameworks, retrieval-augmented generation, APIs, orchestration tools and enterprise platforms.
- Integrate AI solutions with Brookfield systems and infrastructure to ensure enterprise-wide interoperability.
- Build reliable, scalable data pipelines to ingest, transform & validate data - ensuring quality & availability for AI training, inferencing and production workflows
- Build reusable APIs and integration layers that enable AI capabilities to be consumed across applications.
- Manage the operational support of AI solutions including deployment, configuration and observability across Brookfield environments.
- Work closely with internal Brookfield technology teams including cybersecurity, cloud, data, application & project teams to translate requirements into working solutions.
- Test AI models, APIs, frameworks and platforms for accuracy, latency, cost, scalability, integration fit and operational readiness.
- Document implementation details, APIs, dependencies, deployment steps, operational procedures and known limitations.
Key Deliverables- Deployed, tested and enterprise-integrated AI services, APIs and solutions validated for reliability, security and performance.
- Validated, documented data pipelines supporting AI training, inference and production workflows.
- Connectors, API contracts and integration layers linking AI capabilities to business applications.
- Reusable service templates and implementation patterns adopted across AI projects
- CI/CD workflows for AI models and service deployments across all environments.
- Monitoring and observability setup through dashboards, alerts and logging for AI workloads closely monitoring performance, costs and reliability.
- Hardened AWS and Azure infrastructure and security configurations for AI workloads.
- Evaluation reports benchmarking AI models, APIs and frameworks for accuracy, latency, costs and scalability.
- Architecture-aligned technical documentation, including API references, deployment runbooks and known limitations
- Operational runbooks for incident response, troubleshooting and maintenance of AI workloads.
Required Experience- 3-5+ years in software engineering, data engineering or ML engineering roles
- Hands-on experience building and deploying AI/ML solutions in production environments (not just POCs/notebooks)
- Experience translating prototypes or proof-of-concepts into scalable production-grade AI based solutions
- Working experience with AWS and/or Azure, particularly AI/ML services (e.g., SageMaker, Bedrock, Azure ML, Azure Foundry)
- Experience with IAM, networking & security configuration for cloud workloads.
- Experience with containerization and orchestration (Docker, Kubernetes/ECS/EKS)
- Practical experience with LLMs, prompt engineering and agent orchestration frameworks (e.g. Langchain/LangGraph, MS Agent Framework/Semantic Kernel)
- Experience implementing RAG (retrieval-augmented generation) architectures, including vector databases (Azure AI Search, AWS OpenSearch, Bedrock KB, AWS DocumentDB)
- Familiarity with model serving and inference patterns, including real-time, batch and API based approaches.
- Strong API development experience (REST, GraphQL)
- Proficiency in Python with CI/CD pipeline experience (GitHub Actions, Azure DevOps)
Skills & Qualifications- Bachelor's degree in computer science, Software Engineering, Data Science or related field
- Proficiency in Python, with familiarity in JavaScript/TypeScript
- Skilled in LLMs, prompt engineering, RAG architectures, vector databases and agent orchestration frameworks.
- Proficient in REST/GraphQL API build, microservices and integration patterns
- Proficient in building ETL/ELT pipelines and validating data using cloud native tools like AWS Glue or Azure Data Factory
- Proficient in CI/CD pipelines, containerization (Docker, Kubernetes/ECS/EKS), and MLOps practices including model deployment, versioning, and monitoring
- Strong problem solving and analytical thinking with a required ability to translate ambiguous requirements into working solutions
- Effective technical communicator with a required ability to clearly communicate solutions to both technical and non-technical stakeholders
- Demonstrated decisiveness taking ownership of technical decisions and solutions willing to make calculated judgement calls under ambiguity rather than defaulting to indecision.
- Adaptable to work with evolving AI tools/frameworks in a fast-changing landscape
Preferred Qualifications- AWS Certified Machine Learning Engineer - Associate
- AWS Certified Generative AI Developer - Professional
- Azure AI Cloud Developer Associate
- Azure AI App and Agent Developer Associate
Salary Range: C$105K-120K
Position Opening Reason:New Position
