MegazoneCloud
Technology
Sr.AIFDE
Neural analysis suggests this role is
optimal for Senior candidates.
“Sr. AI FDE at MegazoneCloud. Skills: AI adoption, Agentic developer tooling, LLM-powered applications, Cloud AI services. Embed with customer teams. Translate business problems into AI architectures”
What You'll Achieve.
Drive adoption of AI; Deliver production AI systems; Achieve measured adoption; Own outcomes that stick; Meet performance targets; Meet security targets; Meet cost-efficiency targets; Ensure AI solutions are genuinely used; Meet adoption targets; Meet business impact targets; Meet ROI targets
Industry & Context.
Troubleshoot complex issues
What They're Looking For.
Must Have
8–10+ years engineering production software, Hands-on experience building applications on AWS or Google Cloud, Demonstrated experience building with LLMs, Hands-on experience with agentic coding tools, Proficiency in Python, Proven client-facing communication skills, Experience mentoring engineers, Grasp of DevSecOps, SRE, and FinOps principles, Experience architecting data platforms, Integrate AI/ML services, Independently set up and configure cloud AI services
Nice to Have
Azure a plus, Experience leading technology-adoption programs, Experience leading developer-enablement programs, AWS Solutions Architect Professional certification, Google Professional Cloud Architect certification, Azure Solutions Architect Expert certification, AI/ML specialty certifications a plus
What You'll Do.
Embed with customer teams
Translate business problems into AI architectures
Own AI outcomes end to end
Design LLM-powered applications
Build LLM-powered applications
Deploy LLM-powered applications
Integrate with client data stores
Integrate with client APIs
Integrate with client security controls
Stand up AWS environments for AI
Stand up Google Cloud environments for AI
Enable Amazon Bedrock
Configure Amazon Bedrock
Enable Google Vertex AI
Configure Google Vertex AI
Configure model access
Configure service quotas
Drive enterprise adoption of agentic tooling
Secure rollout of agentic tooling
Manage identity isolation
Manage tenant isolation
Integrate agentic tooling with SDLC
Integrate agentic tooling with CI/CD
Enable developers with agentic tooling
Lead change management
Build golden-path templates
Build enablement assets
Define success metrics
Instrument success metrics
Rapidly prototype proofs-of-concept
Iterate prototypes into production systems
Establish reusable accelerators
Establish reference implementations
Establish AI delivery harnesses
Champion shift-left security
Champion responsible AI
Champion FinOps practices for AI
Mentor engineers during build
Troubleshoot complex issues
Serve as escalation point
Support pursuit teams
Present technical vision
Present adoption strategy
Publish internal knowledge articles
How You'll Work.
Team & Collaboration
Customer teams; Cloud engineers; Cross-functional teams
Communication Scope
Client-facing communication; Executive presentations; Technical vision; Adoption strategy
Full Job Description
JOB DESCRIPTION At MegazoneCloud, we help the world’s most innovative companies adopt AI that actually delivers — not pilots that stall, but production systems that get used. As an AI Forward Deployed Engineer (FDE), you embed directly with global clients to drive the adoption of leading AI platforms and agentic developer tooling: generative and agentic AI solutions on AWS and Google Cloud, and agentic coding tools from Anthropic (Claude Code), OpenAI (Codex), and AWS (Kiro). You sit at the intersection of engineering and customer success — rapidly prototyping, integrating with client environments, and owning solutions from proof-of-concept through production and into real, measured adoption. This is an ownership role: you are accountable for outcomes that stick, not just deliverables that ship. You’ll apply best-practice automation — infrastructure as code (IaC), CI/CD, and DevSecOps — to deliver AI workloads that meet demanding performance, security, and cost-efficiency targets. KEY RESPONSIBILITIES • Embed with customer teams to translate business problems into production AI architectures spanning models, data, application, and operations — owning the outcome 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. • Stand up the AWS and Google Cloud environments needed for AI services — enabling and configuring Amazon Bedrock, SageMaker, Google Vertex AI, and related resources (IAM roles, networking, model access, and service quotas) self-sufficiently within client guardrails, partnering with dedicated cloud engineers for deeper foundational account and landing-zone setup. • Drive enterprise adoption of agentic developer tooling (Anthropic Claude Code, OpenAI Codex, AWS Kiro): secure rollout, identity and tenant isolation, SDLC and CI/CD integration, and the developer enablement that turns licenses into measurable
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