Job Description :Our client is looking for a
Agentic AI Engineer
in Toronto, ON
Must Have Primary Skills :
Overview:
Client are seeking a hands-on Agentic AI Platform Engineer with
deep experience in Python, Generative AI frameworks,
and containerized deployments on Kubernetes (OCP,
Azure, AWS).
This is a 100% coding role,
focused on designing, developing, and deploying production-grade AI platform
components — from LLM orchestration to secure, scalable, multi-agent systems.
Nice To Have Secondary Skills :
- Design,
code, and deploy Python-based microservices and frameworks
enabling orchestration of LLM-driven agents.
- Build
and maintain containerized AI workloadsusing Docker and Kubernetes
(OpenShift, EKS, AKS).
- Develop APIs,
SDKs, and Python libraries that power GenAI and agentic workloads
across RBC business lines.
- Implement end-to-end
orchestration for agent workflows, integrating frameworks such
as LangChain, Semantic Kernel, or Haystack.
-
Integrate
and operationalize MCP-Context-Forge for context
management, orchestration, and inter-agent communication.
-
Embed observability,
monitoring, and governance into all platform services
(Prometheus, Grafana, OpenTelemetry).
- Ensure secure
and compliant AI operationsthrough Kubernetes-native policies, RBAC,
and network isolation.
- Collaborate
closely with data scientists, AI researchers, and DevOps teams to
productionize models and agent workflows.
- Prototype,
benchmark, and deploy LLM pipelines on multi-cloud environments (OCP,
Azure, AWS).
- Continuously
enhance developer experience by contributing to internal Python
SDKs, deployment automation, and CI/CD pipelines.
Proven Experience In :Required
Qualifications :
- Expert-level
Python developer — strong track record of building
frameworks, SDKs, or orchestration systems.
- Hands-on
experience coding and deploying GenAI /
LLM-powered applications using LangChain, Semantic
Kernel, or custom agent frameworks.
- Deep
expertise in containerization and Kubernetes:
-
Proficient in Docker, Helm,
and Kubernetes manifests (Deployments, Services,
ConfigMaps, Secrets).
- Experienced with OpenShift (OCP), Azure
AKS, and/or AWS EKS for production-grade
deployments.
- Familiar with Kubernetes networking, security
(RBAC, NetworkPolicies), and monitoring.
-
Strong
understanding of CI/CD pipelines and automation tools
such as GitHub Actions, ArgoCD, or Jenkins.
- Familiarity
with observability stacks(Prometheus, Grafana, Loki,
OpenTelemetry).
- Hands-on
experience with microservices design, API development,
and event-driven orchestration.
- Solid
understanding of LLM system design, context management,
and retrieval-augmented generation (RAG) architectures.
- Comfortable
working across hybrid and multi-cloud environments with
secure service connectivity.
Preferred
Qualifications:
- Experience
developing Python SDKs, internal APIs, or developer tools for
enterprise platforms.
- Familiarity
with model serving frameworks(KServe, Ray Serve, BentoML) and
distributed AI orchestration.
- Knowledge
of service mesh architectures(Istio, Linkerd) and policy
enforcement in Kubernetes.
- Experience
integrating MCP-Context-Forge or similar orchestration
technologies.
- Background
in financial services, particularly in secure AI
deployment or regulated environments.