Java software engineer director software engineering Jobs

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- ₹60,00,000 - ₹90,00,000 a year
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Trimont Services India Pvt Ltd —Hyderabad, Telangana
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Qualcomm —Bengaluru, Karnataka
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ADP —Jayanagar, Bengaluru, Karnataka
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Experian —Hyderabad, Telangana
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Quick apply
Senior Software Engineer - JAVA
Two95 International Inc. —Bengaluru, Karnataka
- ₹20,00,000 - ₹22,00,000 a year
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Quick apply
Engineering Manager
VALOR PAYTECH INDIA PRIVATE LIMITED —Chennai, Tamil Nadu
- Full-time
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Quick apply
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Post Trade Technology Engineering Lead/Sr. Associate Director, Software Engineering
HSBC Global Services Limited —Pune, Maharashtra
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Senior Director of Software Engineering - Java, AWS, RESTful, Containers

JPMorganChase -
Hyderabad, Telangana

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Job details

Full-time
1 day ago

Qualifications

CI/CD
Cloud infrastructure
Kubernetes
Kanban
Test automation
Java
AWS
Docker
PostgreSQL
Neo4j
Continuous integration
REST
Software development
APIs
Agile
Kafka
GraphQL
AI
gRPC
Communication skills
Identity & access management
SDLC

Full job description

JOB DESCRIPTION

Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let's push the boundaries of what's possible together.

As a Senior Director of Software Engineering at JPMorganChase within the Chief Data and Analytics Organization, you lead multiple technical areas, manage the activities of multiple teams, and collaborate across technical domains. Your expertise is applied cross-functionally to evolve firm wide Data Mesh, AI/ML and GenAI, and Data Governance platform. You will play a pivotal role in driving and delivering the strategic objectives, and will collaborate with many cross-functional teams to deliver scalable, high-performance, and user-centric platform. Your expertise will guide the development of key platform components and frameworks that align with the strategic goals of the organization.

Job responsibilities

Define and own the delivery strategy and roadmap for large-scale, cross-team engineering programmes from planning and resourcing through risk mDefine and own the delivery strategy and roadmap for large-scale, cross-team engineering programmes from planning and resourcing through risk management, dependency tracking, and stakeholder communication serving as the primary interface between Engineering, Product, and Executive leadership on programme status, trade-offs, and escalations
Partner with Principal Engineers and Architecture leads to translate technical vision into actionable delivery plans with clear milestones
Lead delivery through periods of organisational or technical change, including platform migrations, team restructures, and process transitions, maintaining momentum and morale
Accountable for quality and production stability, encompassing test automation strategy, performance benchmarking, release readiness, incident management, and SLA adherence
Establish and refine delivery frameworks, governance models, and reporting cadences appropriate for the scale and complexity of each programme
Identify systemic delivery risks early, including technical debt, resource gaps, and cross-team dependencies. Drive resolution before they escalate into blockers
Drive continuous improvement in engineering delivery practices, including retrospectives, delivery metrics, cycle-time analysis, and process optimisation
Mentor and develop technical managers and delivery leads, building a strong leadership pipeline within the engineering organisation
Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorised tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernisation, resilience engineering, and incident response acceleration; establishes guardrails for validation, security, resiliency, traceability, and reuse
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorised AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments

Required qualifications, capabilities, and skills

15+ years of experience in end-to-end technical project/programme delivery, with 5+ years at Director level
Proven track record delivering complex, multi-team programmes on time and at scale
Comfortable navigating conversations across the full stack, including API design (REST, gRPC, GraphQL), AWS cloud infrastructure (EKS, networking, serverless, IAM), CI/CD pipelines, container orchestration (Kubernetes, Docker), event streaming (Kafka, Kinesis), CDN strategies, front-end ecosystem, observability tooling (Datadog, CloudWatch, SLOs/SLIs), databases at scale (PostgreSQL, Neo4j), security and compliance, and QA automation strategies
Able to read and critically assess architecture diagrams and documentation, both front-end and back-end, challenge assumptions, and ask incisive questions in design reviews with technical fluency to evaluate architectural trade-offs: consistency models, service boundaries, cost vs resilience, build vs buy, and to hold engineering teams accountable for sound decisions without needing to dictate the solution
Fluent in modern delivery methodologies (Agile, Scrum, Kanban) with the pragmatism to adapt frameworks to context rather than enforce rigid process
Exceptional communication skills: able to distil engineering complexity into clear, concise narratives for non-technical stakeholders at all levels
Effective at building consensus and driving outcomes across organisations with competing priorities
Track record of building and developing high-performing delivery teams
Skilled at influencing without authority, earning trust from engineers, product owners, and executives alike
Experience leading multi-organisation adoption of agentic AI-enabled engineering operating models (using enterprise-authorised tools within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams
Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies

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