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MegazoneCloud is seeking a Senior AI Forward Deployed Engineer (FDE) to embed directly with global enterprise clients and drive production adoption of AI platforms and agentic developer tooling. You will work at the intersection of engineering and customer success, translating business problems into production AI architectures and owning outcomes from proof-of-concept through measured adoption.
Key responsibilities include:
- Embed with customer teams to design, build, and deploy LLM-powered applications (RAG pipelines, agentic workflows, orchestration frameworks) integrated with client data stores, APIs, and security controls, owning end-to-end outcomes.
- Stand up AWS and Google Cloud environments for AI services (Amazon Bedrock, SageMaker, Google Vertex AI), independently configuring IAM, networking, model access, and service quotas within client guardrails.
- Drive enterprise adoption of agentic developer tooling (Anthropic Claude Code, OpenAI Codex, AWS Kiro) through secure rollout, identity isolation, SDLC integration, and developer enablement.
- Lead adoption and change management by building golden-path templates, enablement assets, and team workflows to ensure AI solutions are genuinely used.
- Define and instrument success metrics (adoption, business impact, ROI) and iterate post-launch until targets are met.
- Rapidly prototype proofs-of-concept and iterate them into production-grade systems alongside customers.
- Establish reusable accelerators, reference implementations, and AI delivery harnesses applicable across engagements.
- Champion shift-left security, responsible AI, and FinOps practices for AI workloads, including token-cost and model-routing discipline.
- Mentor engineers during build and deployment; troubleshoot complex issues spanning models, infrastructure, application, and data layers.
- Serve as escalation point during go-live and hyper-care phases.
- Support pursuit teams with solution diagrams, scoping, and engagement estimation.
- Present technical vision and adoption strategy to C-suite and enterprise architects.
- Publish blog posts, white papers, and internal knowledge articles.
The role emphasizes ownership, client-facing impact, and driving measurable adoption of AI technologies in production environments.
REQUIREMENTS:
- 8–10+ years of production software engineering with hands-on experience on at least one major cloud platform (AWS or Google Cloud preferred; Azure a plus).
- Demonstrated experience building with LLMs (Anthropic Claude, OpenAI, Gemini) including prompt engineering, RAG, and agentic systems.
- Hands-on experience with agentic coding tools (Claude Code, Codex, Kiro) and practical understanding of rollout and adoption across engineering organizations.
- Proficiency in Python and at least one additional language (TypeScript/JavaScript, Java, Go).
- Proven client-facing communication skills; ability to defend architectural and model decisions with executives and engineers; ability to drive adoption through influence.
- Experience mentoring engineers.
- Strong grasp 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 (Amazon Bedrock, SageMaker, Google Vertex AI; IAM, networking, model access, quotas) on AWS and/or Google Cloud.
- Exposure to serverless patterns, event-driven architectures, MCP integration, and vector databases.
- Experience leading technology-adoption or developer-enablement programs is a strong plus.
- Bachelor's degree in Computer Science or similar.
- Certifications required: AWS Solutions Architect Professional, Google Professional Cloud Architect, or Azure Solutions Architect Expert (one required, multiples preferred); AI/ML specialty certifications a plus.
