at J.P. Morgan
Bulge Bracket Investment BanksPosted 4 days ago
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**Director of Software Engineering (Bengaluru, India) at JPMorgan Chase.** Lead cross-functional tech teams driving multiple complex post-trade projects. Govern agentic AI-enabled engineering and SDLC automation; influence stakeholders. Expertise in software engineering (10+ yrs), cloud native, and data technologies like Databricks, Snowflake, AWS (Glue, KMS), Spark (Python/Java), SQL required. Proven leadership, talent development, and hiring experience. Drive impact across departments, balancing innovation with secure, scalable AI solutions. Key responsibilities include: - Lead tech implementation for functional objectives. - Govern AI-enabled engineering, automating SDLC workflows. - Apply software development lifecycle tools for capacity unlock initiatives. - Deliver technical solutions across multiple businesses. - Influence and collaborate with stakeholders. Keywords: Software Engineering Director, Post-Trade Tech, AI-Enabled Engineering, SDLC, Cloud-Native, AWS, Databricks, Snowflake, Data Lakes, Spark, Python, Java, SQL.
- Compensation
- Not specified
- City
- Bengaluru
- Country
- India
Currency: Not specified
Full Job Description
Location: Bengaluru, Karnataka, India
As a Director of Software Engineering at JPMorgan Chase within the Post trade tech, you lead a technical area and drive impact within teams, technologies, and projects across departments. Utilize your in-depth knowledge of software, applications, technical processes, and product management to drive multiple complex projects and initiatives, while serving as a primary decision maker for your teams and be a driver of innovation and solution delivery.
Job responsibilities
- Leads technology and process implementations to achieve functional technology objectives
- Accountable for decisions that influence teams resources, budget, tactical operations, and the execution and implementation of processes and procedures
- 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.
- Carries governance accountability for coding decisions, control obligations, and measures of success such as cost of ownership, maintainability, and portfolio operations
- Delivers technical solutions that can be leveraged across multiple businesses and domains
- Influences peer leaders and senior stakeholders across the business, product, and technology teams
- Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise ( NAMR/APAC India/ LATAM/ Hong Kong)
- Experience developing or leading cross-functional teams of technologists
- Experience with hiring, developing, and recognizing talent
- 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.
- Practical cloud native experience
- Experience working on Databricks/Snowflake.
- Experience in building Data Lakes using AWS and Hands-on experience in AWS Glue, AWS KMS etc.
- Experience in Spark leveraging Python or in-depth experience in Java.
- Experience in writing SQL queries
- Expertise in Computer Science, Computer Engineering, Mathematics, or a related technical field
- Past work experience of working on Data transformation programs
