Agentic AI Platform Engineer (PL862)

Location: Toronto, ON Work Model: Hybrid — 2 days/week downtown Toronto # of roles: 3 Paralucent is seeking an experienced Agentic AI Platform Engineer to join our team and support a major client in the banking sector. In this role, you will be responsible for the hands-on design, development, integration, and execution of AI-driven initiatives focused on agentic AI, cloud platforms, enterprise workflows, and scalable platform modernization. This opportunity is ideal for a technically strong engineer who can translate delivery plans, architecture direction, business requirements, and platform strategy into working solutions. You will work closely with the Delivery Manager, architects, technical leads, client stakeholders, and cross-functional teams to build, test, and deploy agentic AI-enabled capabilities within a complex enterprise environment involving cloud technologies, enterprise integrations, governance requirements, and production-readiness expectations. The successful candidate will play a critical role in turning strategy and technical vision into practical, secure, scalable, and measurable AI-enabled solutions. Key Responsibilities Agentic AI Solution Development Design, develop, and implement agentic AI workflows, AI-enabled services, prototypes, and production-ready platform components. Build solutions that support enterprise workflows, business process transformation, intelligent automation, and human-in-the-loop execution. Implement agentic patterns involving task planning, tool/API usage, workflow orchestration, validation, exception handling, and escalation. Platform Engineering & Cloud Development Develop cloud-native applications, services, APIs, and integrations using AWS and/or Azure. Build reusable platform components, connectors, services, and integration patterns that support scalable AI platform delivery. Partner with architects and technical leads to ensure solutions align with enterprise architecture, security, cloud, and operational standards. AI / LLM Implementation Implement solutions using Artificial Intelligence (AI), Generative AI (GenAI), Large Language Models (LLMs), and agentic AI frameworks or patterns. Develop prompt strategies, structured outputs, tool-calling workflows, retrieval-augmented generation patterns, and AI validation approaches. Support accuracy, reliability, auditability, and safe execution through testing, guardrails, logging, and human review patterns. Enterprise Integration & Workflow Automation Integrate AI-enabled workflows with enterprise applications, APIs, databases, document repositories, and workflow platforms. Build automation and integration components that connect AI capabilities to business processes and operational systems. Support secure access, identity, data flow, logging, monitoring, and compliance-aware implementation within a regulated enterprise environment. Delivery Execution & Technical Collaboration Work closely with the Delivery Manager to execute against delivery plans, milestones, sprint priorities, and release timelines. Break down technical requirements into tasks, estimates, dependencies, and implementation steps. Participate in discovery, design, development, pilot, release, and production-readiness activities with clear technical accountability. Quality, Testing & Production Readiness Write clean, maintainable, secure, and well-documented code. Develop unit tests, integration tests, validation routines, and AI output evaluation approaches. Support deployment, release readiness, operational handoff, troubleshooting, root cause analysis, and continuous improvement. Required Qualifications Proven experience as a Software Developer, AI Engineer, Platform Engineer, Cloud Developer, Full-Stack Developer, or similar hands-on technical role within complex enterprise technology environments. Demonstrated experience designing and building software solutions, APIs, integrations, workflow automations, or platform components. Experience developing solutions within technically complex environments involving cloud platforms such as AWS and/or Azure, enterprise integrations, and modern software delivery practices. Practical experience or strong exposure to Artificial Intelligence (AI), Generative AI (GenAI), Agentic AI, LLM-enabled applications, intelligent automation, or AI-assisted workflow solutions. Strong programming experience with languages such as Python, TypeScript, JavaScript, Java, C#, or similar modern development languages. Ability to translate business requirements, architecture direction, and delivery plans into working technical solutions. Experience working with APIs, databases, cloud services, enterprise applications, document repositories, or workflow platforms. Strong understanding of software development lifecycle practices, including design, development, testing, deployment, and support. Ability to effectively partner with Delivery Managers, architects, technical leads, business analysts, QA teams, and client stakeholders. Strong problem-solving, communication, and execution skills in ambiguous, fast-moving enterprise environments. Preferred Qualifications Experience building or implementing agentic AI applications using frameworks or patterns such as Semantic Kernel, LangChain, AutoGen, CrewAI, Azure AI Foundry, AWS Bedrock Agents, OpenAI Assistants, or similar technologies. Experience supporting initiatives involving Artificial Intelligence (AI), Generative AI (GenAI), Agentic AI, intelligent automation, workflow modernization, or platform modernization programs. Experience working within financial services, banking, or other highly regulated enterprise environments. Exposure to lending operations, servicing, workflow management, process execution, or enterprise operations is considered an asset but is not required. Familiarity with retrieval-augmented generation, embeddings, vector databases, document processing, knowledge retrieval, and enterprise search patterns. Familiarity with secure software development, cloud security, identity/access management, auditability, and compliance-aware delivery. Experience with DevOps practices, CI/CD pipelines, Git-based workflows, infrastructure-as-code, containers, and cloud deployment patterns. Familiarity with Jira, ServiceNow, Confluence, Azure DevOps, GitHub, or similar delivery and operational tooling. Experience supporting pilots, proofs of concept, MVPs, production deployments, platform stabilization, and operational transitions. Core Competencies Agentic AI Engineering Software Development & Platform Execution Cloud Platform Development — AWS and/or Azure AI / GenAI / LLM Implementation API & Enterprise Integration Workflow Automation Prompt Engineering & AI Orchestration Retrieval-Augmented Generation & Knowledge Retrieval Secure, Reliable, Production-Ready Engineering Technical Problem Solving Agile Delivery Execution Quality Engineering & Testing Release Readiness & Operational Handoff Cross-Functional Collaboration Communication, Ownership & Delivery Discipline