oracle
Lead Software Engineer - (SRE principles , AI tools, AWS)
JPMorgan Chase
Bengaluru, IND
Full-time
Senior · 5+ years experience
Onsite
Discovered 2 weeks ago
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- Ensure reliability, scalability, and performance of AI-assisted application and platform operations.
- Design and implement AI-driven solutions for intelligent alerting, noise reduction & auto-correlation systems..
- Build and maintain observability, monitoring, and telemetry for AI application and platforms.
- 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.
- Build and support automation for alerting, anomaly detection, and self-healing workflows.
- Collaborate with engineering, and other stakeholders to drive operational excellence.
- Mentor and guide engineers on AIOps standards and operational excellence.
- Define and execute the roadmap for AI-assisted SRE and observability.
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Demonstrates strong experience in SRE, DevOps, or Platform Engineering roles.
- Strong hands-on experience with AWS (ECS, Lambda, API Gateway, Bedrock, CloudWatch, RDS, EKS).
- Hands-on experience with AWS Bedrock, OpenAI, or LLM APIs.
- Expertise in observability tools: OpenTelemetry, Grafana, Prometheus, ELK, CloudWatch.
- Experience with CI/CD tools (GitHub Actions, Jenkins, Spinnaker ).
- Proven track record in automation, operational tooling, and event-driven workflows.
- In-depth understanding of distributed systems, microservices, and cloud architectures.
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) 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 considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
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