Lead Software Engineer Java Spring boor Gen AI

JPMorgan Chase; Mumbai; Presencial; INR 4.500.000 - 7.500.000; Jornada completa

Descripción de la vacante

JPMorgan Chase within the CORPORATE TECHNOLOGY team seeks a Lead Software Engineer to design, develop, and deliver innovative software solutions with an agile team. You will work on secure, scalable systems across the SDLC and contribute to a culture of growth and technical excellence.

You will lead end-to-end architecture, implement AI/ML capabilities, and ensure high performance and reliability in a regulated environment.

Formación

• Formal training or certification on software engineering concepts and 9+ years applied experience.
• Experience designing and delivering enterprise solutions end-to-end.
• Expertise in Java/Spring Boot microservices, API design, and testing practices.
• Proficiency with Python for services, automation, and data/ML integration.

Responsabilidades

• Own end-to-end solution architecture across applications, APIs, data, integrations, and environments (dev to 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.

Conocimientos

Java 11/17+

Spring Boot

Python 3.x

Distributed systems

REST/gRPC

Kafka

Data architecture

MLOps basics

CI/CD

Security & resiliency

Communication

Educación

Bachelor's degree in Computer Science or related field

Herramientas

Kubernetes

Redis

Git

Terraform

Descripción del empleo

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 to 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 skills‑able 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).