Lead development of agentic AI systems, LLM and NLP applications, MCP integrations, scalable data pipelines, and cloud-based client-facing solutions for financial services. Architect distributed systems with strong observability, security, regulatory compliance, and responsible AI controls. Govern AI-assisted software development practices, coach engineering teams, and establish standards for secure coding, testing, validation, and automation. Collaborate with product and business stakeholders to deliver reliable, scalable AI-driven technology.
Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorganChase within the Asset and Wealth Management, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
As a Senior Lead Software Engineer at JPMorganChase within the Asset and Wealth Management, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
- Design and build autonomous agentic AI workflows capable of multi-step reasoning, tool use, and task execution with appropriate human-in-the-loop guardrails aligned with emerging enterprise patterns such as OpenAI Frontier and Anthropic Claude Cowork, and implement Model Context Protocol (MCP) integrations to enable AI agents to securely connect to and interact with external data sources, APIs, and enterprise tools in real time.
- Architect and implement prompt-based models on Large Language Models (LLMs) for NLP tasks tailored to financial services use cases such as intelligent search, conversational interfaces, and advisory support.
- Produce architecture and design artifacts for complex, distributed applications while ensuring design constraints, including latency, throughput, and regulatory requirements, are met and implement observability, monitoring, and feedback loops for agentic AI systems to track agent behavior, detect hallucinations, and ensure reliability in high-stakes financial applications
- Build and maintain scalable data pipelines and data processing workflows for both structured and unstructured data, leveraging cloud services to support LLM-based features and real-time client interactions ensuring seamless, low-latency experiences.
- Drive adoption and governance of approved AI-assisted engineering practices and Software Development Life Cycle toolchain across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Partner closely with product, design, and business stakeholders to translate client needs and business requirements into scalable AI-driven technical solutions.
- Ensure all client-facing solutions adhere to strict security, privacy, and regulatory standards applicable to the financial services industry, including data protection, model governance, and responsible AI practices, particularly as regulators increase scrutiny of autonomous AI agents in financial services.
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
- Experience building agentic AI systems with multi-step reasoning, tool orchestration, and autonomous task execution within guardrailed environments
- Familiarity with agentic AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar libraries
- Strong programming proficiency in Python or Java, with demonstrated experience building AI/ML-powered applications
- Proven experience designing and developing client-facing applications with a focus on usability, performance, and reliability at scale
- Experience developing microservices and integrating NLP or LLM models into production software applications, and hands-on experience with cloud platforms (AWS or Azure) for AI/ML model deployment, data processing, and infrastructure management
- Solid understanding of the Software Development Life Cycle (SDLC) and agile methodologies including CI/CD, application resiliency, and DevSecOps, and experience working in a large corporate or financial services environment, with familiarity in navigating complex stakeholder landscapes and regulatory frameworks
Preferred qualifications, capabilities, and skills
- Experience with both Java and Python
JPMorganChase Mumbai, Maharashtra, IND Office
B11, B9, B10, Nirlon Knowledge Park, near Mrinal Tai Gore Flyover, Cama Industrial Estate, Mumbai, Maharashtra, India, 400063
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