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2 Days ago

Lead Software Engineer - SRE

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Pune, Maharashtra, India
Energy & Utilities, Energy & Utilities
Full-Time
JPMorganChase

Overview

Job Description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. Lead and conduct resiliency design reviews, break up complex problems, and act as a technical lead for medium to large sized products

Assume a critical role in defining the future of a globally recognized firm and have a direct and significant effect in a realm tailored for top achievers in site reliability.

As a Lead Site Reliability Engineer at JPMorgan Chase within the International Consumer Bank, you hold a leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issues facing them. Take lead and conduct resiliency design reviews, break up complex problems into digestible work for other engineers, act as a technical lead for medium to large-sized products, and provide advice and mentoring to other engineers.

Job responsibilities

  • Demonstrates and champions site reliability culture and practices and exerts technical influence throughout your team
  • Leads initiatives to improve the reliability and stability of your team’s applications and platforms using data-driven analytics to improve service levels
  • Collaborates with team members to identify comprehensive service level indicators and stakeholders to establish reasonable service level objectives and error budgets with customers
  • Demonstrates a high level of technical expertise within one or more technical domains and proactively identifies and solves technology-related bottlenecks in your areas of expertise
  • Acts as the main point of contact during major incidents for your application and demonstrates the skills to identify and solve issues quickly to avoid financial losses
  • Documents and shares knowledge within your organization via internal forums and communities of practice

Required qualifications, capabilities, and skills

  • Formal training or certification on software engineering concepts and 5+ years n reliability, scalability, performance, security, enterprise system architecture, toil reduction, and other site reliability best practices
  • Fluency in at least one programming language such as (e.g., Python, Java Spring Boot, .Net, etc.)
  • Deep knowledge of software applications and technical processes with emerging depth in one or more technical disciplines
  • Proficiency and experience in observability such as white and black box monitoring, SLO alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc.
  • Proficiency in continuous integration and continuous delivery tools (e.g., Jenkins, GitLab, Terraform, etc.)
  • Experience with container and container orchestration (e.g., ECS, Kubernetes, Docker, etc.)
  • Experience with troubleshooting common networking technologies and issues
  • Ability to identify and solve problems related to complex data structures and algorithms
  • Drive to self-educate and evaluate new technology
  • Ability to teach new programming languages to team members
  • Ability to expand and collaborate across different levels and stakeholder groups
  • Experience leading developers in a high impact fast paced environment.
  • Proficient in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API.
  • Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations, and fine-tune models for optimal performance in NLP applications.
  • Strong collaboration and communication skills to work effectively with geographically spread cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
  • Strong problem-solving and analytical skills with emphasize on attention to detail.
  • Experience with cloud platforms, for deploying and scaling AI/ML models.
  • Must have AWS Cloud knowledge
    LMs and executing experiments to push the capability limits of LLM models and enhance their dependability.
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