Senior Cloud Architect, Delivery (GenAI)

• BA/BS degree in Computer Science, Mathematics, or a related technical field, or equivalent practical experience.
• Additional data or AI certifications (e.g., AWS/GCP data certifications, reputable AI/ML programs such as Stanford, Coursera, Udacity, MIT, eCornell).

Expanded AI/ML & Dev Experience

• Experience with modern RLHF, advanced fine-tuning techniques, and hybrid AI architectures.
• Familiarity with Hugging Face or similar open-source ecosystems integrated with AWS.
• Prior experience as a ML Engineer, Data Scientist, or AI-focused Architect in a consulting or SaaS environment.

Tooling & Process

• Experience with JIRA or similar tools for tracking work across delivery and product-feedback cycles.
• Exposure to Agile practices and frameworks commonly used for SaaS and cloud delivery.

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• 4+ years of experience architecting, deploying, and managing cloud-based AI/ML solutions, including production workloads.
• Proven track record designing and operating large, distributed systems on AWS, selecting appropriate services and patterns to meet business and technical goals.

• Be the trusted cloud engineer customers lean on for high‑impact technical optimization work across cost
• and performance.
• Design and help implement solutions that:
• improve cost efficiency (rightsizing, reservations/commitments, storage optimization, etc.)
• increase reliability and resilience (HA/DR architectures, SLO/SLA‑aware designs)
• strengthen security posture (IAM, network segmentation, data protection, least‑privilege)
• reduce operational toil (automation, self‑service, guardrails, policy enforcement)
• Plan and deliver structured engagements such as Cloud Optimization Sessions, cost/efficiency/performance workshops, security posture or reliability reviews, and architecture deep dives / "well‑architected" style assessments.
• Respond to Expert Inquiry / support requests that require deep cloud engineering expertise, ensuring high‑quality, well‑explained resolutions.
• ML / GenAI – deploying and operating ML/GenAI workloads (training and inference), GPU utilization, scaling, and cost control; MLOPS and integrating workloads with monitoring, logging, and FinOps; safe and efficient use of managed AI services.
• Builder – Product Feedback & Contribution
• Turn one‑off field work into reusable assets that improve both customer outcomes and the product itself.
• Convert one‑off customer solutions into Gravel Roads - reusable patterns such as playbooks, Terraform modules, CloudFlow templates, cloud diagrams, Composer Recipes -> DCI Insights, and internal /external documentation.
• Provide structured feedback to the DoiT Product and Engineering teams on:
• product gaps and friction points discovered in real‑world usage
• new opportunities for automation and workload lenses within DCI
• telemetry and tracking that would make future FDE work more efficient
• Contribute directly to DCI where appropriate - from feature requests and feedback, to contributing code, to owning specific DCI features end‑to‑end.
• Build agent skills, scripts, and internal tooling that codify your expertise and scale it across the team.
• Contribute to internal enablement: share learnings via documentation, demos, office hours, or training sessions for other FDEs and Customer Success team members.
• Operate as an embedded technical partner inside the account team.
• Work in the account team model alongside Customer Success Managers (CSMs), Account Managers (AMs) to deliver impactful outcomes.
• Own the technical depth lane: technical deployment & integration, automation & platform adoption, signal‑based proactive engagement, and most importantly, repeatable Cloud Optimization solutions.
• Partner with customers' engineers, architects, and FinOps teams to translate vague pain points into concrete technical optimization plans — and help them ship changes that stick and create continuous value.
• Co‑deliver complex or multi‑domain engagements with peer FDEs (for example, infra + data + ML/GenAI), reviewing and refining designs, and engagement plans together.
• Communicate complex technical topics clearly to both engineers and non‑technical stakeholders (FinOps, finance, leadership), and maintain clear documentation of architectures, decisions, and implemented changes so customers and fellow FDEs can sustain and build on your work.
• Contribute to a culture of continuous improvement within the global FDE community through design reviews, internal forums, enablement sessions, and experimentation.
• Advanced proficiency with AWS services relevant to AI/ML and GenAI.
• Hands-on experience with Amazon Bedrock for deploying and scaling foundation models and Generative AI workloads.
• Experience fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker (including JumpStart).
• Strong prompt engineering skills and familiarity with rigorous model evaluation (quality, safety, performance).
• Understanding of agentic capabilities and patterns for AI agents that autonomously perform tasks and integrate with existing systems.
• Experience with Amazon Q Business and Amazon Q Developer (or similar tools) to accelerate insight generation and development workflows.
• In-depth knowledge of Amazon SageMaker components such as Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify for bias detection and interpretability.
• Proficiency integrating TensorFlow, PyTorch, and other ML frameworks with SageMaker for model development, fine-tuning, and deployment.
• Experience with distributed training (multi-GPU or multi-node) and performance optimization for inference.
• Strong data-engineering skills on AWS: Amazon S3, AWS Glue, Lake Formation, Redshift for AI/ML data pipelines.
• Experience building end-to-end AI/ML workflows using services like AWS Lambda, Step Functions, API Gateway, and containerized deployments on Amazon EKS / AWS Fargate.
• Hands-on experience with CI/CD for AI/ML using AWS CodePipeline, CodeBuild, SageMaker Pipelines, or similar.
• Proficiency in monitoring and operating AI systems using Amazon CloudWatch and SageMaker Model Monitor.
• Strong understanding of AI governance, security, and compliance on AWS, including IAM, KMS, and data privacy patterns.
• Familiarity with AI ethics and bias detection/mitigation (e.g., using SageMaker Clarify or similar tools).
• Working knowledge of Google Cloud AI tools (e.g., Vertex AI, Cloud AutoML, BigQuery ML) sufficient to reason about multi-cloud architectures and integration points.
• Proven ability to mentor peers, run enablement sessions, and collaborate across Sales, CS, and Product.
• Excellent communication skills across technical and business audiences; able to simplify complex ideas and influence decisions.
• Natural ownership mentality: you escalate early, resolve fast, and own the outcome.
• Demonstrated ability to work effectively in a remote-first, global environment.