Agentic AI Forward Deployment Engineering Lead
New
CanadaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 6–12 years
- Required Skills
- CRMLLMDistributed Systems
Requirements
- 6–12 years of experience in Solution Architecture, Enterprise Delivery, Professional Services, Technical Program Management, Implementation Consulting, or related fields.
- Proven success leading complex enterprise software deployments and large-scale customer implementations.
- Strong ability to gather business requirements and translate them into scalable technical architectures and deployment plans.
- Deep understanding of APIs, enterprise integrations, distributed systems, data flows, and system architecture principles.
- Experience managing cross-functional teams across engineering, product, quality assurance, integration, and customer-facing functions.
- Excellent communication, stakeholder management, facilitation, and executive presentation skills.
- Strong project leadership, analytical thinking, and problem-solving capabilities with a focus on execution excellence.
- Ability to navigate ambiguity and manage multiple high-priority initiatives simultaneously.
- Experience with AI-powered solutions, workflow automation, customer experience platforms, or enterprise transformation projects is highly desirable.
- Familiarity with Agentic AI, Large Language Models (LLMs), conversational AI, prompt engineering, workflow orchestration, or autonomous systems is a strong advantage.
Responsibilities
- Lead discovery workshops and stakeholder sessions to understand business processes, operational challenges, customer journeys, and automation opportunities.
- Design scalable Agentic AI architectures, workflows, governance frameworks, escalation paths, and implementation strategies aligned with customer objectives.
- Own the end-to-end deployment lifecycle, ensuring projects progress successfully from kickoff through production launch and post-deployment optimization.
- Serve as the primary technical delivery lead, coordinating activities across engineering, product, integration, QA, customer success, and customer teams.
- Define integration strategies connecting enterprise systems, APIs, CRM platforms, customer service tools, and third-party applications.
- Review solution designs, security requirements, authentication models, operational readiness plans, and technical feasibility assessments.
- Establish quality standards, acceptance criteria, testing methodologies, and deployment best practices to ensure successful outcomes.
- Act as a trusted advisor to technical and executive stakeholders, providing guidance on AI adoption, governance, automation strategy, and ROI.
- Develop reusable implementation frameworks, deployment playbooks, and solution templates to improve delivery efficiency and consistency.
- Capture lessons learned and collaborate with internal teams to drive continuous platform and process improvements.
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