Sr Lead Software Engineer - Agentic AI Solutions

JPMorganChase; Bengaluru; On-site; INR 2,500,000 - 5,000,000; Full time

Job summary

JPMorganChase in Bengaluru seeks a Senior Lead Software Engineer within Asset and Wealth Management to drive AI-enabled product delivery and secure, scalable cloud solutions.

You will mentor engineers, shape architecture, and collaborate with product managers to implement multi-agent AI systems on AWS, integrating with tools across the SDLC.

The role emphasizes responsible AI, rigorous testing, and delivering high-impact technology across teams.

Qualifications

• Formal training or certification in software engineering with 5+ years of experience.
• Experience in software engineering using AI technologies.
• Strong hands-on skills in Python, Pydantic, FastAPI, LangGraph, and vector databases.
• Experience with LLM integration and AI agent frameworks like LangChain/LangGraph/Autogen.
• Solid understanding of CI/CD, Terraform, Kubernetes, Docker, and APIs.
• Familiarity with observability/monitoring platforms.

Responsibilities

• Collaborate with engineers and product managers to define requirements and deliver robust solutions.
• Design and implement LLM-driven agent services on AWS for design, code generation, docs, tests, and observability.
• Develop orchestration and communication layers between agents using frameworks like A2A SDK.
• Integrate AI agents with Jira, Bitbucket, Github, Terraform and monitoring platforms.
• Contribute to system design, SDK development, and data pipelines for agent intelligence.
• Provide technical leadership and mentorship to junior engineers.
• Promote AI-assisted engineering practices and validate AI outputs for correctness and security.

Skills

Software engineering fundamentals

AI engineering

Python

Pydantic

FastAPI

LangGraph

Vector Databases

LLMs integration

Prompt engineering

AI agent frameworks

CI/CD

Terraform

Kubernetes

Docker

APIs

Observability

Leadership

Responsible AI

Independent problem solving

Cloud native

Azure

GCP

MLOps

Tools

AWS

EKS

Lambda

S3

Terraform

Kubernetes

Docker

LangGraph

Langchain

A2A SDK

Jira

Bitbucket

Github

Auto Gen

Job description

Job Description

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

Job Description

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

• Works closely with software engineers, product managers, and other stakeholders to define requirements and deliver robust solutions.
• Designs and Implement LLM-driven agent services for design, code generation, documentation, test creation and observability on AWS
• Develops orchestration and communication layers between agents using frameworks like A2A SDK, LangGraph, or Auto Gen
• Integrates AI agents with toolchains such as Jira, Bitbucket, Github, Terraform and monitoring platforms
• Collaborates on system design, SDK development and data pipelines supporting agent intelligence
• Provides technical leadership, mentorship, and guidance to junior engineers and team members.
• 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.
• Drives decisions that influence the product design, application functionality, and technical operations and processes

Required Qualifications, Capabilities, And Skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Experience in Software engineering using AI Technologies
• Strong hands-on skills in Python, Pydantic, FastAPI, LangGraph, and Vector Databases for building RAG based AI agent solutions integrating with multi-agent orchestration frameworks and deploying end-to-end pipelines on AWS (EKS, Lambda, S3, Terraform)
• Experience with LLMs integration, prompt/context engineering, AI Agent frameworks like Langchain/LangGraph, Autogen, MCPs, A2A.
• Solid understanding of CI/CD, Terraform, Kubernetes, Docker and APIs
• Familiarity with observability and monitoring platforms
• Strong analytical and problem-solving mindset.
• 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.
• Ability to tackle design and functionality problems independently with little to no oversight
• Practical cloud native experience

Preferred Qualifications, Capabilities, And Skills

• Experience with Azure or Google Cloud Platform (GCP).
• Familiarity with MLOps practices, including CI/CD for ML, model monitoring, automated deployment, and ML pipelines.