Lead Software Engineer - AWS SRE

JPMorganChase; Hyderabad; Vor Ort; INR 320.000 - 700.000; Vollzeit

Zusammenfassung

JPMorganChase in Hyderabad is seeking a Lead Software Engineer to join the Deposits team within Consumer & Community Banking. You will be a core technical contributor, shaping architecture and delivery of trusted, scalable technology products in a secure, agile environment.

You will lead Live Site Management and cloud production practices, advance SRE, observability, AI-assisted tooling, and multi-region resilience to ensure production health, cost efficiency, and rapid incident response across

Qualifikationen

• Formal training or certification in software engineering plus 5+ years of experience.
• Production engineering roles across AWS, SRE/operations, and software development with leadership ownership.
• Hands-on AWS ECS and EC2 production experience with deployments and tuning.
• Expert-level AWS networking and security (VPC, IAM, TLS, etc.).
• Infrastructure as Code experience with Terraform, CloudFormation or CDK.
• Strong SRE leadership defining SLIs/SLOs and alerting standards.
• Advanced observability and incident response with Dynatrace, Splunk, Grafana.
• Proven automation, code-quality practices, and test strategies.
• Experience establishing production readiness standards and runbooks.
• Familiarity with AI-assisted development tools and safe adoption practices.
• Understanding of responsible AI use in engineering workflows.

Aufgaben

• Lead Live Site Management and cloud production practices, including readiness, governance, incidents, and risk reduction.
• Define and improve SRE practices, observability, and automated anomaly detection across apps and infra.
• Shape the end-to-end AWS cloud architecture strategy and optimize cloud costs across products.
• Advance resiliency with multi-region, active-active, failover/failback, and disaster recovery.
• Mature automation and IaC strategy; integrate AI across workflows.
• Establish production health, availability, and performance measures for leadership insight.
• Drive major incident response, RCAs, and corrective actions to prevent recurrence.
• Promote enterprise AI-assisted engineering practices and robust validation standards.
• Apply SDLC tooling knowledge to maximize automation value and quality.

Kenntnisse

Software engineering fundamentals

AWS production engineering

AWS ECS

EC2 production

AWS networking

IAM security

Terraform

CloudFormation

CDK

SRE leadership

Dynatrace

Splunk

Grafana

Automation tooling

Code review

Production readiness

AI-assisted development

Responsible AI

Tools

AWS

ECS

EC2

Dynatrace

Splunk

Grafana

Terraform

CloudFormation

CDK

IAM

KMS

Jobbeschreibung

Job Description

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

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 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 where applicable).
• Strong Infrastructure as Code (IaC) expertise (Terraform , CloudFormation/CDK ): reusable module design, environment promotion, state management, drift detection, and safe rollout patterns.
• Strong SRE leadership: defines SLIs/SLOs, sets alerting standards, applies error budgets, drives reliability roadmaps, and balances reliability vs delivery with stakeholders.
• Advanced observability and incident response using Dynatrace, 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 Demonstrated software 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.