Job description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Lead Software Engineer at JPMorganChase within the Sales Assistant Technology Team, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. You'll be part of the team to drive, own and build key components of Markets Sales AI Platform to on-board agentic workflows into our system in a federated way, turning frontier GenAI capabilities into reliable, high-leverage agentic systems that transform how we respond to inbound client requests. This is a hands-on role for someone who thrives on ambiguity, ships quickly, and is energized by hard technical challenges. Job responsibilities • Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems • Drive/Build/Own agentic platform with UI, orchestration, routing, memory, training ground and evaluations frameworks end-to-end: design, prototype, and productionize multi-step LLM agents that retrieve context and generate accurate, well-structured responses • Drive applied AI by evaluating emerging techniques (tool use, planning, retrieval, evaluation frameworks, fine-tuning, prompt optimization) and integrating the best into production • Improve quality systematically via evals, error analysis, and feedback loops that convert subjective issues into measurable fixes • Partner cross-functionally with quant research, product, and engineering to understand their agents and provide solutions on-board on to our platform in a federated way. • Build and maintain production-grade code and systems that are observable, robust, and scalable • Contribute to technical direction and standards for agent design, evaluation, and safe deployment • 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. • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture Required qualifications, capabilities, and skills • Formal training or certification on software engineering concepts and 5+ years applied experience • Degree in Computer Science, Data Science, Machine Learning or related field or related experience • Strong coding skills (Python) and comfort owning production code • Hands-on experience in AWS, ECS, Bedrock. • Agentic development tool kit example Google ADK must have • Familiarity with evaluation frameworks, LLM observability, or fine-tuning open-weight models • Hands-on experience building with LLMs (agent frameworks, tool use, RAG, prompt engineering, evals) • Strong understanding of modern GenAI capabilities, failure modes, and practical mitigation strategies • Strong ownership and drive mindset with a focus on improving what’s broken with clear design principles • Experience working in a fast pace environment Preferred qualifications, capabilities, and skills • OpenSearch, Vector dbs and Postgres nice to have Build Markets Sales Assistant AI Platform to on-board agentic workflows into our system

Requirements

Must-haves

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
Drive applied AI by evaluating emerging techniques (tool use, planning, retrieval, evaluation frameworks, fine-tuning, prompt optimization) and integrating the best into production
Improve quality systematically via evals, error analysis, and feedback loops that convert subjective issues into measurable fixes
Partner cross-functionally with quant research, product, and engineering to understand their agents and provide solutions on-board on to our platform in a federated way.
Build and maintain production-grade code and systems that are observable, robust, and scalable
Contribute to technical direction and standards for agent design, evaluation, and safe deployment
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Degree in Computer Science, Data Science, Machine Learning or related field or related experience
Strong coding skills (Python) and comfort owning production code
Hands-on experience in AWS, ECS, Bedrock.
Agentic development tool kit example Google ADK must have
Familiarity with evaluation frameworks, LLM observability, or fine-tuning open-weight models
Hands-on experience building with LLMs (agent frameworks, tool use, RAG, prompt engineering, evals)
Strong understanding of modern GenAI capabilities, failure modes, and practical mitigation strategies
Strong ownership and drive mindset with a focus on improving what’s broken with clear design principles
Experience working in a fast pace environment
Build Markets Sales Assistant AI Platform to on-board agentic workflows into our system

Nice-to-haves

Preferred qualifications, capabilities, and skills
OpenSearch, Vector dbs and Postgres nice to have

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

agileaillmmachine learningpythonawspostgres

Platform: oracle-cloud
Effort: medium