Forward Deployed Engineer

Caylent

7h•Remote

About The Position

Caylent is a cloud native services company that helps organizations bring the best out of their people and technology using Amazon Web Services (AWS). They provide a full-range of AWS services including workload migrations and modernization, cloud native application development, DevOps, data engineering, security and compliance. Caylent is a global company and operates fully remote with employees in Canada, the United States, and Latin America. The Anthropic Business Unit is a dedicated AI services practice built in partnership with Anthropic, and is Anthropic’s first pure-play delivery partner. This role, a Forward Deployed Engineer (FDE), is the technical engine of AI engagements, owning hands-on delivery including rapid prototyping, running evaluations, configuring Claude Code tooling, and building production-grade AI systems in client environments. FDEs live inside clients’ codebases, standups, and architecture decisions, owning technical outcomes. There are two FDE archetypes: Technology-Workforce FDE, specializing in AI-enabled developer tooling, AIOps, and LLM-native product builds for engineering, product, and IT organizations; and Operational FDE, specializing in workforce automation, process transformation, and AI-augmented business operations for operational and business-unit workforces.

Requirements

5+ years of software engineering, with meaningful time in a client-facing or embedded delivery context
Hands-on experience with Claude Code or equivalent AI-assisted development tools, including configuration, prompt-based instructions, and workflow automation.
Hands-on depth in one or more of: LLM application development, cloud-native architecture (AWS preferred), Kubernetes, or backend systems at scale
Experience with rapid prototyping. You can take an ambiguous problem and have a working proof-of-concept in days, not weeks
Familiarity with evaluation frameworks for AI systems: defining metrics, running evals, and using results to iterate on solutions
Proven ability to run independently inside a client environment, managing your own priorities, stakeholders, and delivery quality while aligning with the broader engagement strategy
Strong communicator: technically precise with engineers, clear and confident with technical leadership; conceptual and easy to understand for the business; adjusts depth to the audience
High tolerance for ambiguity; able to start delivering value before every requirement is defined

Nice To Haves

Experience building production AI systems: RAG pipelines, agents, fine-tuned models, or LLM-native applications
Direct experience with Claude Code’s extensibility model: custom skills, hooks, MCP servers, and prompt architecture. Claude Certified Architect Certification preferred.
Background in a consulting firm, systems integrator, or professional services environment
Familiarity with SOW-based delivery and scope management in a professional services context
AWS certifications: Solutions Architect Professional, DevOps Engineer Professional, or equivalent
Experience designing and running technical workshops or enablement sessions for engineering teams

Responsibilities

Own the technical delivery within AI engagements, working alongside the strategy and architecture lead
Build rapid prototypes that validate AI approaches early. Prove feasibility before committing to full implementation
Design and execute evaluation frameworks: define metrics, run evals against client use cases, and use results to iterate on solutions and inform strategic recommendations
Configure and customize Claude Code tooling tailored to each client’s codebase, coding standards, and development practices
Design and build production-grade AI systems: agents, data pipelines, AI-augmented workflows, and platform capabilities in the client’s codebase, not a sandbox
Own the technical relationship with client engineering teams: you are the primary technical contact from IC to engineering leadership, building the trust that earns you a seat in hard conversations
Run technical discovery sessions, architecture reviews, and hands-on workshops with engineering stakeholders; translate strategic direction into scoped, deliverable work
Surface technical risks and opportunities. Your field observations shape the engagement strategy
Transfer lasting capability: build as if you’re handing off tomorrow, with documentation, runbooks, and working software that stands on its own after you leave
Feed the practice: contribute reusable patterns, evaluation templates, and field-tested IP back to the team at engagement close. This is how the business gets smarter from your engagements
Support pre-sales scoping and technical discussions alongside the account team
Mentor junior engineers and contribute to the delivery methodology
Collaborate with the engagement team to refine delivery patterns and improve handoffs between strategy and implementation