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Lead Software Engineer Java Spring boor Gen AI

ExperiencedNo visa sponsorship
J.P. Morgan logo

at J.P. Morgan

Bulge Bracket Investment Banks

Posted 5 days ago

No clicks

**Lead Software Engineer - Java Spring Boot & Gen AI**: Lead design of end-to-end architecture, own API and integration patterns, and drive Java/Spring Boot and Python development for AI/ML services. Steer complex issue resolution and Ensure secure, scalable, and tagent-compliant solutions. Requires 9+ years experience in software engineering, Java 11/17+, Spring Boot, Python, and AI/ML. Leverage Kubernetes, cloud-native patterns, and MLOps for production-grade AI solutions.

Compensation
Not specified

Currency: Not specified

City
Mumbai
Country
India

Full Job Description

Location: Mumbai, Maharashtra, India

Join us to advance your software engineering career while building impactful technology solutions. Grow your skills and make a difference with a collaborative team.

As a Lead Software Engineer at JPMorgan Chase within the CORPORATE TECHNOLOGY team, you will design, develop, and deliver innovative software solutions. You will collaborate with an agile team to enhance technology products in a secure and scalable way. You will gain hands-on experience across the software development lifecycle. You will contribute to a supportive team culture focused on growth and technical excellence.

Job responsibilities 

  • Own end-to-end solution architecture across applications, APIs, data, integrations, and environments (dev prod).
  • Convert business requirements into target-state architecture, HLD, and LLD with clear trade-offs and documented decisions.
  • Define integration patterns and cross-service contracts (REST/gRPC, event-driven), including versioning, compatibility, and SLAs.
  • Provide hands-on technical leadership by contributing Java/Spring Boot and Python code for reference implementations, critical paths, and POCs to production.
  • Lead design spikes and performance investigations to validate approaches and resolve complex technical risks.
  • Drive complex production issue triage and root cause analysis, ensuring sustainable fixes and operational learning.
  • Establish and maintain engineering standards through reusable patterns, templates, and shared components.
  • Design and deliver production patterns for AI/ML capabilities (batch + real-time inference, feature pipelines, evaluation, monitoring).
  • Partner with Data Science teams to move models from experimentation to reliable, scalable, observable services.
  • Implement responsible AI controls (traceability, testing/evaluation, approvals, and appropriate human oversight where required).
  • Define and deliver NFRs and operational excellence (availability, latency, throughput, scalability, resiliency, RTO/RPO, capacity planning, observability/SLOs/runbooks) with secure-by-design and SDLC/audit compliance.

Required qualifications, capabilities and skills 

  • Formal training or certification on software engineering concepts and 9+ years applied experience
  • Strong hands-on experience designing and delivering enterprise solutions end-to-end.
  • Java 11/17+ expertise, including Spring Boot microservices, API design, and testing practices.
  • Python 3.x expertise for services, automation, and data/ML integration.
  • Proven distributed systems experience: microservices, event-driven architecture, and messaging/streaming (Kafka or equivalent).
  • Strong data architecture fundamentals: relational/NoSQL patterns, caching (e.g., Redis), data consistency, and schema evolution.
  • Practical experience productionizing AI/ML: inference patterns, model packaging/serving, and monitoring/drift fundamentals.
  • Working knowledge of MLOps concepts and operational model lifecycle management.
  • DevOps delivery mindset: CI/CD, automated testing (unit/integration/contract), and release/rollback strategies.
  • Strong security and resiliency mindset suitable for regulated environments. Excellent communication and influence skillsable to explain trade-offs and align stakeholders.

Preferred qualifications, capabilities and skills 

  • Kubernetes/container platforms and cloud-native patterns (autoscaling, config/secrets, service-to-service security).
  • GenAI experience (LLMs, RAG, vector search, evaluation frameworks, prompt/model governance) where applicable.
  • Modernization experience (monolith decomposition, strangler patterns, incremental migration).
  • Advanced production readiness/observability practices (SRE-style monitoring, readiness reviews, operational rigor).
Lead solution design and delivery; build scalable Java/Python AI services, staying hands-on through execution.

Lead Software Engineer Java Spring boor Gen AI

Compensation

Not specified

City: Mumbai

Country: India

J.P. Morgan logo
Bulge Bracket Investment Banks

5 days ago

No clicks

at J.P. Morgan

ExperiencedNo visa sponsorship

**Lead Software Engineer - Java Spring Boot & Gen AI**: Lead design of end-to-end architecture, own API and integration patterns, and drive Java/Spring Boot and Python development for AI/ML services. Steer complex issue resolution and Ensure secure, scalable, and tagent-compliant solutions. Requires 9+ years experience in software engineering, Java 11/17+, Spring Boot, Python, and AI/ML. Leverage Kubernetes, cloud-native patterns, and MLOps for production-grade AI solutions.

Full Job Description

Location: Mumbai, Maharashtra, India

Join us to advance your software engineering career while building impactful technology solutions. Grow your skills and make a difference with a collaborative team.

As a Lead Software Engineer at JPMorgan Chase within the CORPORATE TECHNOLOGY team, you will design, develop, and deliver innovative software solutions. You will collaborate with an agile team to enhance technology products in a secure and scalable way. You will gain hands-on experience across the software development lifecycle. You will contribute to a supportive team culture focused on growth and technical excellence.

Job responsibilities 

  • Own end-to-end solution architecture across applications, APIs, data, integrations, and environments (dev prod).
  • Convert business requirements into target-state architecture, HLD, and LLD with clear trade-offs and documented decisions.
  • Define integration patterns and cross-service contracts (REST/gRPC, event-driven), including versioning, compatibility, and SLAs.
  • Provide hands-on technical leadership by contributing Java/Spring Boot and Python code for reference implementations, critical paths, and POCs to production.
  • Lead design spikes and performance investigations to validate approaches and resolve complex technical risks.
  • Drive complex production issue triage and root cause analysis, ensuring sustainable fixes and operational learning.
  • Establish and maintain engineering standards through reusable patterns, templates, and shared components.
  • Design and deliver production patterns for AI/ML capabilities (batch + real-time inference, feature pipelines, evaluation, monitoring).
  • Partner with Data Science teams to move models from experimentation to reliable, scalable, observable services.
  • Implement responsible AI controls (traceability, testing/evaluation, approvals, and appropriate human oversight where required).
  • Define and deliver NFRs and operational excellence (availability, latency, throughput, scalability, resiliency, RTO/RPO, capacity planning, observability/SLOs/runbooks) with secure-by-design and SDLC/audit compliance.

Required qualifications, capabilities and skills 

  • Formal training or certification on software engineering concepts and 9+ years applied experience
  • Strong hands-on experience designing and delivering enterprise solutions end-to-end.
  • Java 11/17+ expertise, including Spring Boot microservices, API design, and testing practices.
  • Python 3.x expertise for services, automation, and data/ML integration.
  • Proven distributed systems experience: microservices, event-driven architecture, and messaging/streaming (Kafka or equivalent).
  • Strong data architecture fundamentals: relational/NoSQL patterns, caching (e.g., Redis), data consistency, and schema evolution.
  • Practical experience productionizing AI/ML: inference patterns, model packaging/serving, and monitoring/drift fundamentals.
  • Working knowledge of MLOps concepts and operational model lifecycle management.
  • DevOps delivery mindset: CI/CD, automated testing (unit/integration/contract), and release/rollback strategies.
  • Strong security and resiliency mindset suitable for regulated environments. Excellent communication and influence skillsable to explain trade-offs and align stakeholders.

Preferred qualifications, capabilities and skills 

  • Kubernetes/container platforms and cloud-native patterns (autoscaling, config/secrets, service-to-service security).
  • GenAI experience (LLMs, RAG, vector search, evaluation frameworks, prompt/model governance) where applicable.
  • Modernization experience (monolith decomposition, strangler patterns, incremental migration).
  • Advanced production readiness/observability practices (SRE-style monitoring, readiness reviews, operational rigor).
Lead solution design and delivery; build scalable Java/Python AI services, staying hands-on through execution.