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Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.\nJob responsibilities\nProvides authoritative technical guidance and architectural direction to business stakeholders, technical teams, contractors, and vendors - with a focus on workflow automation, AI/ML platforms, and cloud-native solutions on AWS, Azure and GCP.\nDesigns, engineers, and implements end-to-end workflow automation solutions and AI/ML pipelines, from architecture through production deployment\nDevelops secure, high-quality production code; reviews, debugs, and optimizes code written by others - leveraging GitHub Copilot and AI-assisted development practices to accelerate delivery\nDrives architectural decisions that influence product design, application functionality, and technical operations - including infrastructure-as-code standards using Terraform across AWS environments\nServes as a function-wide subject matter expert in AWS cloud architecture, MLOps, workflow orchestration, and intelligent automation\nDefines and enforces best practices for CI/CD pipelines, IaC (Terraform), model lifecycle management, and automated testing within the SDLC\nActively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices - championing AWS Well-Architected principles, Terraform module reuse, and responsible AI/ML governance\nInfluences peers and project decision-makers to evaluate and adopt leading-edge technologies including LLMs, agentic AI frameworks, and cloud-native automation services (e.g., AWS Step Functions, EventBridge, SageMaker, Bedrock)\nSets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.\nApplies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets. Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.\n\nRequired qualifications, capabilities, and skills\nFormal training or certification on software engineeringconcepts and 10+ years applied experience\nHands-on practical experience delivering system design, application development, testing, and operational stability - with demonstrated delivery of production-grade AI/ML and automation solutions\nAdvanced proficiency in Python\nAdvanced knowledge of AI/ML frameworks and tooling and their operationalization in cloud environments\nDeep expertise in AWS cloud services relevant to automation and AI/ML workloads, including but not limited to: Step Functions, Lambda, ECS/EKS, SageMaker, Bedrock, Glue, EventBridge, and IAM\nProficiency in Terraform for infrastructure-as-code, including module development, state management, and multi-environment deployment patterns\nExperience with GitHub Copilot and AI-assisted development workflows; 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