Sr. AI FDE

Megazone Cloud US • Rochester, NY

7d • $145,000 - $165,000

About The Position

At MegazoneCloud, the AI Forward Deployed Engineer embeds directly with global clients to drive adoption of AI platforms and agentic developer tools. The role sits at the intersection of engineering and customer success, rapidly prototyping and integrating solutions in client environments and owning them from proof of concept through production and measured adoption. The engineer applies infrastructure as code, CI/CD, and DevSecOps practices to deliver AI workloads that meet performance, security, and cost-efficiency targets.

The position involves designing and deploying LLM-powered applications and agentic workflows, configuring cloud AI services on AWS and Google Cloud, and enabling secure enterprise adoption of coding tools. The engineer leads change management, defines adoption and ROI measures, develops reusable delivery assets, mentors engineers, supports go-live, and communicates technical vision and adoption strategy to client leaders.

Requirements

• 8–10+ years of experience engineering production software.
• Hands-on experience building applications on at least one major cloud platform; AWS or Google Cloud preferred, with Azure a plus.
• Demonstrated experience building with LLMs such as Anthropic Claude, OpenAI, or Gemini, including prompt engineering, RAG, and agentic systems.
• Hands-on experience with agentic coding tools such as Claude Code, Codex, or Kiro, and practical knowledge of rolling them out and driving adoption across an engineering organization.
• Proficiency in Python and at least one additional language, such as TypeScript/JavaScript, Java, or Go.
• Client-facing communication skills, including the ability to defend architectural and model decisions to executives and engineers and drive adoption through influence.
• Experience mentoring engineers.
• Strong understanding of DevSecOps, SRE, and FinOps principles.
• Experience architecting data platforms and integrating AI/ML services.
• Ability to independently set up and configure cloud AI services and supporting resources on AWS and/or Google Cloud, including IAM, networking, model access, and quotas; foundational account and landing-zone work is done with dedicated cloud engineers.
• Bachelor’s degree in Computer Science or a similar field.
• One certification required: AWS Solutions Architect Professional, Google Professional Cloud Architect, or Azure Solutions Architect Expert.

Nice To Haves

• Exposure to serverless patterns, event-driven architectures, MCP integration, and vector databases.
• Experience leading technology-adoption or developer-enablement programs.
• Multiple listed cloud architect certifications are preferred.
• AI/ML specialty certifications are a plus.

Responsibilities

• Embed with customer teams to translate business problems into production AI architectures spanning models, data, applications, and operations, owning outcomes end to end.
• Design, build, and deploy LLM-powered applications, including RAG pipelines, agentic workflows, and orchestration frameworks integrated with client data stores, APIs, and security controls.
• Set up and configure AWS and Google Cloud environments and AI services, including Amazon Bedrock, SageMaker, Google Vertex AI, IAM roles, networking, model access, and service quotas, within client guardrails and in partnership with cloud engineers for foundational account and landing-zone setup.
• Drive enterprise adoption of Anthropic Claude Code, OpenAI Codex, and AWS Kiro through secure rollout, identity and tenant isolation, SDLC and CI/CD integration, and developer enablement.
• Lead adoption and change management by creating golden-path templates, enablement assets, and team workflows.
• Define and instrument adoption, business impact, and ROI metrics, then iterate after launch until targets are met.
• Prototype proofs of concept and develop them into production-grade systems alongside customers.
• Establish reusable accelerators, reference implementations, and AI delivery harnesses for engagements.
• Promote shift-left security, responsible AI, and FinOps practices, including token-cost and model-routing discipline.
• Mentor engineers during build and deployment and troubleshoot issues across model, infrastructure, application, and data layers.
• Serve as an escalation point during go-live and hyper-care.
• Support pursuit teams with solution diagrams, scoping, and engagement estimation.
• Present technical vision and adoption strategy to C-suite leaders and enterprise architects.
• Publish blog posts, white papers, and internal knowledge articles.

Benefits

• Investment in employee growth and development.
• Servant leadership focused on empowering employees and clearing roadblocks.
• Flat organization offering a direct voice in the technical roadmap and client outcomes.
• A learning culture that treats mistakes as opportunities to learn.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior