Software Engineer II

Are you ready to build your career and gain valuable skills in software engineering? JPMorgan Chase offers an exciting opportunity for a Software Engineer II within the Asset & Wealth Management division in Bengaluru/Bangalore. You will join an agile team focused on enhancing, designing, and delivering state-of-the-art technology products securely, stably, and scalably.

As an emerging member of our software engineering team, you will contribute to the design, development, and technical troubleshooting of multiple components within complex technical products, applications, and systems. This role is designed for growth, providing the experience needed to advance your career.

Job Responsibilities

• Develop Python services, APIs, and tooling to enhance CI/CD processes, deployment orchestration, and developer productivity on AWS.
• Build scalable, secure microservices and batch workflows utilizing AWS services, adhering to best practices for networking, identity, and security.
• Own and manage infrastructure as code (IaC) using Terraform, including module development, state management, and environment provisioning, while establishing standards, review processes, and automation for plans and applies.
• Integrate with enterprise systems and AWS services using Python, creating reusable SDKs, CLI tools, templates, and libraries.
• Implement policy-as-code, audit logging, and compliance controls to enforce Role-Based Access Control (RBAC) and secure secrets management across applications and infrastructure.
• Optimize deployment strategies such as canary and blue/green deployments, including rollbacks, approvals, and gates within CI/CD pipelines.
• Drive automated testing efforts (unit, integration, contract) using Python frameworks, manage test data, and implement continuous quality gates.
• Ensure system reliability and observability through comprehensive logging, metrics, tracing, and alerting, defining Service Level Objectives (SLOs) and error budgets.
• Implement performance tuning and cost optimization across compute, storage, and networking resources.
• Collaborate effectively with product, platform, security, and Site Reliability Engineering (SRE) teams.
• Contribute to roadmap planning, estimation, and delivery, actively managing technical risks, debt, and cross-team dependencies.

Required Qualifications, Capabilities, and Skills

• Formal training or certification in software engineering concepts with 2+ years of applied experience.
• Proficiency in full-stack development and significant experience with CI/CD platforms.
• In-depth experience designing, deploying, and managing solutions on AWS, including VPC, IAM, EC2, S3, Lambda, and core networking/security principles.
• Hands-on expertise in infrastructure-as-code with Terraform for provisioning and managing cloud resources.
• Proficiency in containerization with Docker, including building, optimizing, and securing container images.
• Strong background in orchestrating and scaling workloads using Kubernetes, including RBAC and cluster management.
• Familiarity with multi-cloud environments (AWS, Azure, GCP) and integrating cloud-native services into CI/CD pipelines.
• Expertise in pipeline design, deployment strategies, and release governance within regulated environments.
• A security-first mindset, with experience in secrets management, RBAC, OIDC/SAML, compliance, audit, and policy-as-code (OPA).
• Proficiency in observability tools and practices, including logging, metrics, and tracing (e.g., Datadog, Splunk, Prometheus, OpenTelemetry).
• Strong architectural design, documentation, and stakeholder communication skills.

Preferred Qualifications, Capabilities, and Skills

• Experience with Agentic AI or integrating AI/ML models into CI/CD workflows.
• Familiarity with AI-driven automation, intelligent agents, or Large Language Model (LLM)-based developer tools.
• Experience with API Gateway, container orchestration, serverless architectures, and Terraform Cloud/Enterprise.
• Knowledge of security best practices in AWS, particularly for AI/ML workloads.
• Exposure to prompt engineering, AI workflow orchestration, or embedding AI stages in the Software Development Life Cycle (SDLC).
• A strong interest in continuous learning and experimentation with emerging AI technologies in software engineering.