Lead Software Engineer - Java, Cloud Platforms (AWS), Kafka,Generative AI
JPMorgan Chase17 hours ago
Hyderābād, IndiaStaff+
Responsibilities
- Architect end-to-end AI systems and workflows, including LLM features, RAG, agentic patterns, decision support, APIs, data flows, and operational readiness.
- Lead rapid prototyping, experimentation, product discovery, and evaluation to inform roadmap decisions.
- Develop secure production code, review other engineers’ code, and maintain performance, resiliency, and maintainability standards.
- Define non-functional requirements for AI services, including latency, cost, reliability, and availability.
- Establish AI quality, validation, regression-testing, human-review, guardrail, and safety standards.
- Integrate AI capabilities into enterprise applications and software delivery workflows from prototype through production.
- Partner with Product, Design, Data Science/ML, Risk, governance, and other stakeholders on problem statements, metrics, and acceptance criteria.
- Evaluate models, tools, and vendors for architectural fit, control requirements, and platform integration.
- Improve operational stability through observability, incident learnings, proactive reliability engineering, and automated remediation.
- Lead adoption of authorized AI-assisted engineering practices, including code review, refactoring, test acceleration, and incident analysis.
- Coach engineers on secure, compliant, and validated use of AI-assisted development tools.
Requirements
- Formal training or certification in software engineering concepts and 5+ years of applied experience are stated; the posting also specifies 10+ years of applied software engineering experience, including 3+ years delivering AI/ML and Generative AI solutions in production.
- Advanced programming capability in Java, Python, or TypeScript with strong code quality and testing discipline.
- Hands-on experience designing and delivering LLM solutions using RAG, embeddings, orchestration or tool calling, and prompt or model optimization.
- Experience building AI agents and agentic workflows with measurable outcome tracking.
- Experience with AI frameworks such as LangChain, LangGraph, CrewAI, Semantic Kernel, AutoGen, or equivalent tools and patterns.
- Experience defining evaluation frameworks, guardrails, and validation approaches for correctness, safety, privacy, and resiliency.
- 3+ years building and operating cloud-native production services on AWS and/or Azure; GCP experience is accepted where applicable.
- Experience with MLOps or LLMOps, including model deployment, monitoring, observability, and lifecycle management.
- Experience leading approved AI-assisted software development practices and validating AI outputs for correctness, performance, and security.
- Understanding of responsible AI engineering, data sensitivity, secure input and output handling, resiliency, security, and compliant team adoption.
- Preferred experience with vector databases, large-scale knowledge retrieval, RAG performance optimization, Docker, Kubernetes, platform engineering, AI-assisted SDLC practices, cybersecurity controls, AI governance, enterprise modernization, and technical mentoring.
- Familiarity with Spark, Kafka, Snowflake, and/or Databricks is preferred.
Tech Stack
Categories
About JPMorgan Chase
JPMorgan Chase provides consumer and commercial banking, payments, credit card, wealth management, and corporate and investment banking services to individuals, businesses, institutions, and governments. The public company (NYSE: JPM) earns revenue from interest, fees, trading, and asset management across operations in more than 100 markets. Headquartered in New York City with roots dating to 1799, it serves retail customers and prominent corporate and government clients through brands including Chase and J.P. Morgan.