About The Role

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorganChase within the Corporate Sector - CFS Cloud Enablement Team , you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. 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.

Job responsibilities

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.