Lead Software Engineer - AI and Python

JP Morgan Services India Pvt Ltd; Bengaluru; Presencial; INR 4 000 000 - 7 000 000; Tempo integral

Resumo da oferta

JP Morgan Services India Pvt Ltd in Bengaluru seeks a Lead Software Engineer AI + Python to drive the design and delivery of scalable GenAI applications and agentic AI platforms forFinance use cases. You will lead cloud-native AWS services, establish observability standards, and guide cross-team architectural decisions in a regulated enterprise.

The role requires advanced Python skills, hands-on AI system delivery, and leadership to mentor engineers and ensure governance, security, and cost

Qualificações

• Formal training or certification on software engineering concepts and 8+ years applied experience
• Experience architecting and shipping production large language model applications, including agentic workflows and tool integration patterns
• Experience leading effective use of approved AI-assisted software development tools with validation of outputs
• Strong understanding of responsible AI use in engineering workflows with data sensitivity and security considerations
• Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Autogen, CrewAI, A2A)
• Experience building retrieval-augmented generation solutions using embeddings and semantic search
• Strong fundamentals to deliver cloud-native services on AWS using containers/serverless
• Advanced Python programming skills and production-grade code
• Experience with frontier models on platforms like AWS Bedrock / Vertex AI

Responsabilidades

• Lead architecture and hands-on delivery of scalable agentic AI platforms for enterprise workflows
• Promote enterprise AI practices to improve code quality, delivery speed, and operational outcomes
• Improve automation value with enterprise AI tooling across the SDLC
• Design and build production-grade AI systems including agents, memory patterns, and tool orchestration
• Architect retrieval and context engineering approaches including embeddings and grounding
• Engineer cloud-native AI services on AWS with containers and serverless patterns
• Optimize platform performance and cost controls across latency, throughput, and scalability
• Build well-governed APIs and integrations connecting AI to enterprise platforms
• Establish evaluation and observability frameworks to improve agent quality
• Mentor senior engineers and influence cross-team architecture decisions
• Leverage enterprise AI coding assist tools with peer review and secure coding practices

Conhecimentos

Python

AI/LLM

AWS

Kubernetes

Mentoring

Code review

Distributed systems

LangChain

Security considerations

Ferramentas

AWS Bedrock

Vertex AI

Containers

Descrição da oferta de emprego

Lead Software Engineer AI + Python

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within Corporate Technology, you will lead the architecture and hands-on implementation of scalable GenAI Applications and agentic AI platforms for Finance use cases leveraging Firmwide AI tools & platforms. You will design cloud-native solutions, establish evaluation and observability standards, and drive technical decisions across teams to improve reliability, cost, and developer velocity. The candidate will design cloud-native AWS services and reusable platform capabilities (agents, retrieval/RAG, guardrails, tool orchestration, APIs), while establishing strong evaluation, observability, reliability, security, and cost controls. Ideal candidates have extensive experience, advanced Python, proven delivery of LLM/agentic systems, and technical leadership skills to mentor engineers and drive cross-team architecture standards in a regulated enterprise environment.

Job responsibilities

• Lead the architecture and hands-on delivery of scalable, reliable agentic AI platforms for enterprise workflows


• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.


• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.


• Design and build production-grade AI systems including agents, skills, memory patterns, guardrails, and tool-use orchestration


• Architect retrieval and context-engineering approaches including embeddings, semantic search, grounding, summarization, and prompt/version management


• Engineer cloud-native AI services on AWS using containers and serverless patterns, event-driven messaging, and distributed data stores


• Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls


• Build well-governed APIs and integrations that connect AI capabilities to enterprise platforms, tools, and business processes


• Establish evaluation, experimentation, regression testing, and observability frameworks to continuously improve quality and agent behavior


• Mentor senior engineers and influence engineering direction through code reviews, architecture forums, and cross-team technical leadership


• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.

Required qualifications, capabilities and skills

• Formal training or certification on software engineering concepts and 8+ years applied experience


• Experience architecting and shipping production large language model applications, including agentic workflows and tool integration patterns


• Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices


• Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Autogen, CrewAI and A2A etc)


• Experience building retrieval-augmented generation solutions (embeddings, semantic search, grounding) using Vector databases and managing prompt lifecycle/versioning


• Strong software engineering fundamentals with ability to deliver cloud-native services using containers and serverless designs on AWS


• Advanced python programming skills with experience writing production quality code


• Build systems using frontier models from OpenAI, Anthropic or others leveraging platforms such as AWS Bedrock / Google Vertex AI or other similar platforms


• Proven technical leadership skills, including mentoring, driving architecture decisions, and influencing cross-functional stakeholders


• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.

Preferred qualifications, capabilities and skills

• Experience building standardized evaluation harnesses, automated regression suites, and experimentation platforms for large language model systems


• Hands-on experience with Kubernetes-based deployment patterns and operational excellence practices for high-availability services


• Experience applying privacy, data minimization, and safe AI guardrail patterns in regulated or high-risk environments


• Familiarity with context-efficiency optimization techniques and cost governance for large language model workloads


• Experience building reusable developer platforms, reference architectures, and technical standards across multiple teams