Director Of Software Engineering - Java Python AI

JPMorganChase; Bengaluru; On-site; INR 3,500,000 - 7,500,000; Full time

Benefits offered by this job

Health insurance

Job summary

JPMorganChase in Bengaluru seeks a Director of Software Engineering for the Autonomous Infrastructure and AI4Reliability team. You will lead a senior group, own end-to-end production systems, and set architectural and engineering standards with an AI-native approach.

You will write and review production code, drive reliability, security, and cost-efficiency while guiding the team through on-call responsibilities and scalable, observable operations.

Qualifications

• Hands-on software engineering with production coding experience in Python, Go, Java, C++, or Rust.
• Experience running production systems at scale, including on-call ownership and incident response.
• Experience leading or mentoring engineers and setting technical direction.
• Strong systems thinking, including interfaces, contracts, failure modes, and scale.
• Ability to direct AI tools for real engineering work with sound judgment on AI applicability.
• Security-first mindset, integrating risk from design through production.
• Clear, direct communication with engineers, stakeholders, and peers.
• Outcome orientation focused on impact, reliability, and cost.
• Comfort operating with ambiguity and greenfield scope.
• Inclusive, collaborative leadership to attract and retain senior engineers.

Responsibilities

• Lead and grow a small team of senior engineers, fostering a high-performance, high-ownership culture
• Own the design, delivery, and operation of enterprise-scale infrastructure services from architecture through production
• Write and review production code, maintaining a hands-on approach and setting the bar for engineering quality
• Establish AI-native engineering practices with robust validation standards to ensure speed and correctness
• Participate in an on-call rotation and act as an escalation point for production incidents, building operability
• Analyze and optimize systems for scalability, efficiency, reliability, and performance
• Decompose ambiguous problems into clear, executable work for engineers and AI agents
• Set direction and governance for agentic AI-enabled engineering and SDLC/TLM automation to drive improvements
• Apply knowledge of tools within the SDLC toolchain to improve automation value
• Define success criteria, measure impact, and hold the team accountable to outcomes
• Partner with stakeholders across the infrastructure organization, resolving technical disagreements

Job description

Job Description:

As a Director of Software Engineering in Autonomous Infrastructure and AI4Reliability team you will lead a small, senior team designing, building, and operating tools and services which will manage and heal our infrastructure using AI and agentic flows. You will be a hands-on technical leader, owning production systems end-to-end, setting architecture and engineering standards, and fostering the growth of your team. You’ll work AI-native across the software development lifecycle, ensuring correctness, security, reliability, and cost-effectiveness.

Job Responsibilities:

• Lead and grow a small team of senior engineers, fostering a high-performance, high-ownership culture
• Own the design, delivery, and operation of enterprise-scale infrastructure services from architecture through production
• Write and review production code, maintaining a hands-on approach and setting the bar for engineering quality
• Establish AI-native engineering practices with robust validation standards to ensure speed never compromises correctness
• Participate in an on-call rotation and act as an escalation point for production incidents, building operability and observability from the start
• Analyze and optimize systems for scalability, efficiency, reliability, and performance
• Decompose ambiguous problems into clear, executable work for both engineers and AI agents
• Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse across teams.
• 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 and support capacity unlock initiatives at scale.
• Define success criteria, measure impact, and hold the team accountable to outcomes
• Partner with stakeholders across the infrastructure organization, resolving technical disagreements and proactively raising risks

Required Qualifications, Capabilities, and Skills:

• Hands-on software engineering background with production coding experience in an industry-standard language (e.g., Python, Go, Java, C++, Rust)
• Experience running production systems at scale, including on-call ownership, incident response, and designing for reliability and operability
• Experience leading or mentoring engineers and setting technical direction
• Strong systems thinking, including interfaces, contracts, failure modes, and interactions at scale
• Ability to direct AI tools for real engineering work, with sound judgment on where AI applies and where human expertise is required
• Security-first mindset, integrating risk judgment from design through production
• Clear, direct communication with engineers, stakeholders, and peers
• Outcome orientation, focused on impact, reliability, and cost
• Comfort operating with ambiguity and greenfield scope
• Inclusive, collaborative leadership style, able to attract, grow, and retain strong senior engineers

Preferred Qualifications, Capabilities, and Skills:

• Track record of reducing operational toil and cost through automation and better engineering
• Experience adopting AI-native engineering practices at team or organizational scale
• Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorized tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
• Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse.
• Prior experience in regulated or large-scale enterprise environments in the payment industries
• Experience with greenfield builds and establishing engineering culture

Requirements: