• End-to-End Ownership Inside Strategic Accounts: Own discovery, technical scoping, infrastructure design, build, and production rollout for each design partner and ISV engagement. • Cloud Infrastructure & Compute Orchestration: Build and operate cloud infrastructure that powers customer physical AI workflows. • Platform Services: Build platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking. • Customer Onboarding Infrastructure: Build onboarding infrastructure for pilots including sandbox environments and secure execution. • Reliability, Security & Cost: Optimize cloud cost, utilization, performance, and reliability across workloads. • Cross-FDE Partnership: Partner with Physical AI Systems FDE to support heavy simulation pipelines. • Long-Term Architecture: Define the long-term infrastructure architecture for multi-tenant SaaS and high-throughput physical AI workloads. • Pattern Codification & Productization: Turn customer infrastructure pain into reusable platform capabilities. • Rapid Engineering Velocity: Use modern AI coding tools to compress build timelines. • Field Enablement & Feedback Loops: Co-author reference architectures, solution templates, and technical blogs for broader field support.

• Collaborate closely with fellow engineers, product managers, and stakeholders to design, build, and support robust platform solutions • Architect, deploy, and manage Kubernetes clusters on AWS and/or Azure • Design and implement CI/CD pipelines that deliver high-quality code at speed • Build automated infrastructure with Terraform and other IaC tools • Lead cloud migration and modernization efforts, including re-platforming legacy workloads (Java, .NET, ETL, databases) to modern cloud-native services • Automate scaling, deployment, and management of containerized applications • Monitor, troubleshoot, and fine-tune system performance to ensure reliability, cost-effectiveness, and resilience • Implement proactive monitoring solutions that detect and prevent outages • Translate complex client requirements into scalable, automated solutions • Contribute to team knowledge-sharing, peer reviews, runbooks, and process improvement efforts • Support and maintain secure, compliant, and resilient infrastructure for critical systems

• Design and implement Retrieval-Augmented Generation (RAG) architectures. • Build AI-powered chatbot experiences using AWS Bedrock. • Develop multi-source data ingestion and retrieval pipelines. • Design and optimize embeddings and semantic search strategies. • Build scalable vector search solutions for enterprise knowledge retrieval. • Integrate structured and unstructured data sources into AI workflows. • Develop prompt engineering strategies to improve response quality and accuracy. • Build cloud-native AI services using AWS. • Collaborate with engineering teams to improve AI performance, scalability, and reliability. • Continuously evaluate new AI technologies and best practices for enterprise LLM applications.

• Design and develop enterprise AI solutions using AWS Bedrock. • Build AI-powered workflow automation for complex business processes. • Develop AI agents using AWS Bedrock Agents and Guardrails. • Integrate enterprise platforms, including ServiceNow and SAP. • Develop secure REST API integrations across enterprise applications. • Build Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge bases. • Develop document-processing workflows, including PDF extraction and business-rule validation. • Design cloud-native backend services using AWS. • Build scalable React interfaces for AI-powered business applications. • Develop GenAI CI/CD pipelines and automation workflows. • Implement Responsible AI principles and governance across AI solutions. • Collaborate with engineering and business teams to continuously improve AI capabilities.