MegazoneCloud is the ideal digital transformation partner supporting enterprises in successful Generative AI adoption and cloud-native modernization.
About the role
• 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.
What they're looking for
- • 8–10+ years engineering production software, with hands-on experience building applications on at least one major cloud platform — AWS or Google Cloud preferred (Azure a plus)
- • Demonstrated experience building with LLMs (e.g., Anthropic Claude, OpenAI, Gemini) — prompt engineering, RAG, and agentic systems
- • Hands-on experience with agentic coding tools (e.g., Claude Code, Codex, Kiro), and a practical understanding of how to roll them out and drive adoption across an engineering organization
- • Proficiency in Python and at least one additional language (e.g., TypeScript/JavaScript, Java, Go)
- • Proven client-facing communication skills — able to defend architectural and model decisions with executives and engineers alike, and to drive adoption through influence
- • Experience mentoring engineers
More about this role
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.
• 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...
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