• Provides 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.
• Designs, engineers, and implements end-to-end workflow automation solutions and AI/ML pipelines, from architecture through production deployment
• Develops secure, high-quality production code; reviews, debugs, and optimizes code written by others — leveraging GitHub Copilot and AI-assisted development practices to accelerate delivery
• Drives architectural decisions that influence product design, application functionality, and technical operations — including infrastructure-as-code standards using Terraform across AWS environments
• Serves as a function-wide subject matter expert in AWS cloud architecture, MLOps, workflow orchestration, and intelligent automation
• Defines and enforces best practices for CI/CD pipelines, IaC (Terraform), model lifecycle management, and automated testing within the SDLC
• Actively 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
• Influences 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)
• 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.

• Applies 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.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 10+ years applied experience
• Hands-on practical experience delivering system design, application development, testing, and operational stability — with demonstrated delivery of production-grade AI/ML and automation solutions
• Advanced proficiency in Python
• Advanced knowledge of AI/ML frameworks and tooling and their operationalization in cloud environments
• Deep 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
• Proficiency in Terraform for infrastructure-as-code, including module development, state management, and multi-environment deployment patterns
• Experience with GitHub Copilot and AI-assisted development workflows; ability to evaluate, govern, and scale AI coding tools within an engineering team
• Ability to independently tackle complex design and functionality problems with minimal oversight, driving solutions from ambiguous requirements to production
• Practical cloud-native experience with strong understanding of security, scalability, resilience, and cost optimization in AWS
• Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.