Senior Manager Of Software Engineering- SDET

JPMorganChase; Mumbai; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

JPMorganChase in Mumbai seeks a Senior Manager of Software Engineering to mentor multiple technical teams and guide AI-enhanced testing strategies. You will influence budgets, shape frameworks, and advance operational efficiency across the Payments Technology domain.

You will lead the design of scalable automation, performance testing, and quality metrics, while embedding quality into the SDLC from design to production and ensuring compliance with enterprise standards.

Qualifications

• Formal training or certification in software engineering or related field; 5+ years of applied software engineering experience
• 2+ years of experience leading technologists to manage complex technical items within your domain
• Experience leading multi-team adoption of enterprise-grade AI-assisted development and delivery tools
• Strong understanding of responsible AI use in engineering workflows and data sensitivity considerations
• Experience in software testing/quality engineering and leadership of SDET/QE teams
• Deep hands-on expertise with API automation (Rest Assured) and UI automation (Playwright) at scale
• Strong background in performance/load testing (JMeter) and production performance analysis
• Experience driving AI-enabled testing transformations and automation intelligence across teams
• Strong leadership, stakeholder management, and executive communication
• Deep understanding of CI/CD, DevOps, cloud infrastructure (AWS/Azure/GCP), and microservices architecture
• Experience presenting quality strategy and metrics to senior leadership
• Strong programming background in Java/JavaScript/Python with hands-on contribution when needed

Responsibilities

• Defines and owns enterprise test automation and quality engineering strategy; scales quality org across APAC and LATAM
• Provides hands-on technical leadership in automations frameworks using Rest Assured and Playwright
• Drives performance engineering roadmap with JMeter, Blazemeter, Gremlin; ensures enterprise SLAs
• Champions AI-assisted testing practices to improve velocity and reduce manual effort
• Deploys synthetic testing to detect issues before production impact
• Embedded quality and testability into the SDLC from design through production
• Establishes quality metrics and dashboards for executive audiences
• Champions CI/CD-integrated testing practices for fast feedback
• Acts as escalation point for complex production performance issues
• Sets operating practices for AI-assisted engineering and SDLC automation across teams
• Applies SDLC tooling knowledge to drive efficiency and capacity unlock initiatives

Skills

Leadership

CI/CD

Cloud platforms

Java/JavaScript/Python

SDET/QE leadership

AI-assisted testing

Tools

Rest Assured

Playwright

JMeter

Blazemeter

Gremlin

GenAI tooling

Job description

Job Description:

When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this.

As a Senior Manager of Software Engineering at JPMorganChase within the Commercial & Investment Bank, Payments Technology, you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, as well as anticipate the needs and potential dependencies of other functions within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.

Job responsibilities

• Defines and own theenterprise test automation and quality engineering strategy, aligning with broader engineering and business objectives. Scales and mentor global Quality organization across APAC, LATAM, fostering a “shift Left” culture where quality is shared engineering responsibility
• Provides hands-on technical leadership in designing scalable automation frameworks usingRest Assured(API) andPlaywright(UI/E2E), setting standards and best practices across teams.
• Drives theperformance engineering roadmap— load, stress, scalability, and resilience testing — using tools such as JMeter, Blazemeter, Gremlin ensuring systems meet enterprise SLAs.
• Drives adoption ofAI/GenAI-powered testingpractices across the organization (AI-assisted test generation, self-healing automation, intelligent defect triage, predictive quality analytics) to improve engineering velocity and reduce manual effort.
• Deploys advanced synthetic testing to detect issues and latencies in before they impact a single customer in production.
• Partners with senior engineering, product, and business leadership to embed quality and testability into the SDLC from design through production (shift-left/shift-right practices).
• Establishes and report on quality metrics, KPIs, and executive dashboards (defect escape rate, automation coverage, MTTR, performance benchmarks) to senior stakeholders.
• Champions CI/CD-integrated testing practices, ensuring fast, reliable feedback loops across the release pipeline.
• Acts as an escalation point and technical authority for complex production performance issues and quality risk assessments.
• 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.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience . In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise
• 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.
• Experience in software testing/quality engineering and in a leadership/management capacity (leading SDET/QE teams).
• Deep hands-on expertise withRest Assuredfor API automation andPlaywrightfor UI/E2E automation, with a track record of building frameworks at scale.
• Strong background inperformance/load testingstrategy and execution (JMeter), including capacity planning and production performance analysis.
• Demonstrated experience drivingAI-enabled testing transformation— automation intelligence, GenAI copilots, or ML-based quality analytics. Proven track record building and scaling automation/performance frameworks across multiple teams or business units.
• Strong leadership skills — team building, mentoring, stakeholder management, and executive communication.
• Deep understanding of CI/CD, DevOps practices, cloud infrastructure (AWS/Azure/GCP), and microservices architecture.
• Experience presenting quality strategy and metrics to senior/executive leadership.
• Strong programming background (Java, JavaScript/TypeScript, or Python) with the ability to contribute hands-on when needed.