Jobs at Caylent

Own and manage client relationships, make architectural decisions, run discovery and stakeholder interviews, deliver software or automations, and contribute intellectual property back to the practice.

Build and deploy production AI systems, cloud-native applications, workflow automations, and LLM-enabled products within client environments. Own assigned workstreams, contribute to discovery and architecture, translate ambiguous problems into deliverables, manage risks, and transfer sustainable solutions to clients. Collaborate with technical and business stakeholders, rapidly prototype solutions, and contribute reusable patterns to the AI services practice.

Own client accounts from discovery through close, independently leading stakeholder interviews, architecture, technical delivery, scope management, and expansion opportunities. Build production-grade AI systems, cloud-native applications, backend platforms, or business automations, while ensuring solutions are documented, maintainable, and transferable. Senior engineers communicate effectively with technical teams and executive stakeholders, make autonomous decisions, manage delivery quality, and contribute reusable practices to the AI services organization.

Build and operate enterprise Amazon Connect contact centers and AI agents. Implement generative AI (Bedrock/Claude), serverless backends, CDK IaC, observability, testing, and integrations with CRMs and analytics. Own delivery from code to production, mentor engineers, and support customer-facing technical workstreams.

Lead client cloud architecture engagements, design and implement scalable AWS infrastructure, and guide re-platforming and modernization initiatives. Build Infrastructure as Code, observability, monitoring, automation, and security solutions using AWS and partner tools. Lead cross-functional teams, establish runbooks, conduct code reviews, coach engineers, engage clients, and support continuous improvement. The role also involves technical presentations, documentation, vendor relationships, open-source contributions, and occasional travel.

Lead the product workstream in client AI engagements: assess product teams, design future-state PDLC, coach PMs on AI-native workflows, scope prototypes, write executable specs and Sprint Charters, design PM training, and feed field insights into agent tooling and methodology improvements.

Lead AI engagements, shaping strategy and architecture. Engage with various stakeholders, design AI operating models, and facilitate workshops. Drive expansion opportunities and contribute to internal frameworks.

The AI/ML Engineering Manager leads a team of ML engineers and architects, managing hiring, performance, and customer engagements while providing technical guidance in ML operations and architecture.