Lead Software Engineer - AWS SRE

JP Morgan Chase; Hyderabad; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

JPMorganChase, in Hyderabad, seeks a Lead Software Engineer to drive production reliability for the Deposits team within Consumer & Community Banking. You will lead SRE practices, advance AWS architecture, and champion AI-assisted engineering to boost code quality and delivery speed.

You will own incident response, observability, and multi-region resiliency while shaping cost optimization and automation strategies across product components.

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Production engineering roles spanning AWS platform engineering, SRE/operations, and software development, with leadership ownership of critical services.
• Deep hands-on experience with AWS ECS and EC2 in production: architecture, deployments, scaling, capacity management, AMI strategy, patching, and performance tuning.
• Expert-level AWS networking & security: VPC architecture, routing, Security Groups/NACLs, ALB/NLB, DNS/TLS, IAM least privilege (incl. role design, boundaries/guardrails).
• Strong IaC expertise (Terraform, CloudFormation/CDK): reusable modules, environment promotion, state management, drift detection, safe rollout patterns.
• Strong SRE leadership: defines SLIs/SLOs, sets alerting standards, applies error budgets, drives reliability roadmaps.

Responsibilities

• Lead Live Site Management and cloud production management practices, including production readiness, change governance, incident response, problem management, and operational risk reduction.
• Define and improve SRE practices, including SLI/SLOs, error budgets, capacity planning, and observability across applications and infrastructure with effective logging, metrics, tracing, dashboards, and automated anomaly detection.
• Shape the end-to-end AWS cloud architecture strategy across all product components; strengthen cloud cost strategy and optimization.
• Advance resiliency strategy, including multi-region, active-active, failover/failback, and disaster recovery.
• Evolve automation and infrastructure-as-code strategy; Mature AI integration across engineering and product workflows.
• Raise the bar on shared architectural standards and decision-making; Establish production health, availability, performance, and resiliency measures for actionable insights.
• Drive major incident response, RCAs, corrective actions, and elimination of recurring production issues.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes while ensuring validation standards.
• Applies tooling knowledge in the SDLC with AI-assisted development to improve automation value.

Skills

SRE leadership

AWS cloud

Observability

Incident response

AI-assisted development

Security & resiliency

Education

Software engineering training/certification

Tools

AWS ECS

AWS EC2

Terraform

CloudFormation/CDK

CDK

Dynatrace

Splunk

Grafana

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Deposits team of Consumer & Community Banking Division, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Lead Live Site Management and cloud production management practices, including production readiness, change governance, incident response, problem management, and operational risk reduction.
• Define and improve SRE practices, including service-level indicators, service-level objectives, error budgets, capacity planning, and reliability engineering and Advance observability across applications and infrastructure through effective logging, metrics, distributed tracing, dashboards, alerting, and automated anomaly detection and Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized through automation.
• Shape the end-to-end AWS cloud architecture strategy across all product components ; Strengthen cloud cost strategy and optimization across the product.
• Advance the resiliency strategy, including multi-region, active-active, failover/failback, and disaster recovery.
• Evolve the automation and infrastructure-as-code strategy ; Mature AI integration across engineering and product workflows.
• Raise the bar on shared architectural standards and decision-making across teams ; Sharpen how complex technical strategy is distilled into leadership-facing narratives and Establish production health, availability, performance, and resiliency measures that provide actionable insight to engineering and business leadership
• Drive major incident response, root-cause analysis, corrective actions, and the systematic elimination of recurring production issues.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required qualifications, capabilities, and skills

• A. Formal training or certification on software engineering concepts and 5+ years applied experience
• Production engineering roles spanningAWS platform engineering, SRE/operations, and software development, with leadership ownership of critical services.
• Deep hands-on experience withAWS ECS and EC2in production: architecture, deployments, scaling, capacity management, AMI strategy, patching, and performance tuning.
• Expert-levelAWS networking & security: VPC architecture, routing, Security Groups/NACLs, ALB/NLB, DNS/TLS, IAM least privilege (incl. role design, boundaries/guardrails where applicable).
• StrongInfrastructure as Code (IaC)expertise (Terraform , CloudFormation/CDK ): reusable module design, environment promotion, state management, drift detection, and safe rollout patterns.
• StrongSRE leadership: defines SLIs/SLOs, sets alerting standards, applies error budgets, drives reliability roadmaps, and balances reliability vs delivery with stakeholders.
• Advancedobservability and incident responseusingDynatrace, Splunk, and Grafana: tracing and service flow analysis, log search/correlation (SPL), dashboard/alert tuning, and measurable MTTR reduction.
• leads complex incident triage, performs RCAs, authors blameless postmortems, and ensures corrective actions land via engineering changes and Demonstratedsoftware engineering + code-commit capability: designs/implements automation and platform tooling, enforces code quality/testing, and sets strong code review practices for the team.
• Experience establishing production readiness standards, operational controls, runbooks, dashboards, and measurable service health objectives.
• Demonstrated experience leading effective use of approved AI-assisted software development tools, with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.

Preferred qualifications, capabilities, and skills

• LLMOps / bot operations: evaluation harnesses, regression testing for prompts, rollout strategies, monitoring (quality + latency + cost), and incident handling for bot services.
• ECS advanced patterns: capacity providers, placement constraints/strategies, spot/on-demand mix, draining behavior, deployment tuning.
• Security hardening & governance: secrets management, KMS usage patterns, vulnerability management, and operational controls aligned to regulated environments.
• Cost optimization (FinOps): right-sizing, scaling efficiency, and observability/log ingestion cost controls.