Lead Software Engineer - Large Language Models and Cloud Technologies

Lead Software Engineer - Java/Python, AWS,LLM 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 Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Banks MACRO technology team, you are an integral member of an agile team building secure, stable, and scalable LLM enabled solutions. As a core technical contributor, you design and deliver controlled, well understood LLM assisted components and multi agent workflows across multiple business functions in support of the firms objectives in a regulated environment Job responsibilities Execute creative LLM assisted software solutions, design, develop, and troubleshoot LLM powered applications and services (e.g., retrieval augmented generation, agent workflows, structured extraction, classification) with a willingness to think beyond routine approaches to break down technical problems and deliver measurable outcomes and think in the novel Agentic AI way. 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. Develop data quality rules and controls using LLM, define and enforce guardrails for prompts, retrieved context, model inputs/outputs, and post processing, including PII redaction, toxicity/safety filters, hallucination mitigation, output schema validation, and policy compliance. Provide Level 3 (L3) support for LLM assisted production systems, own complex incidents, model and prompt rollouts/rollbacks, dependency issues (vector stores, embeddings, feature stores), and ensure high availability, reliability, and adherence to SLAs including latency and cost budgets. Support BAU operations for Markets businesses: maintain and evolve LLM use cases supporting markets workflows with disciplined change management, canary releases, A/B tests, and close partnership with product, controls, and operations. Create secure, high quality production code: implement LLM assisted micro services, synchronous and asynchronous inference pipelines (streaming where appropriate), deterministic fallbacks, circuit breakers, and observability for reliability in production. Produce architecture and design artifacts, deliver model cards, system/data lineage, RAG/agent reference architectures, prompt libraries and versioning strategies, evaluation plans, and control evidence ensuring design constraints and regulatory expectations are met during development. Identify hidden problems and patterns, use telemetry, error analysis, prompt and context analytics, and drift detection to improve model selection, prompt strategies, retrieval quality, chunking/embedding strategies, and system architecture. Ensure that model strengths, limitations, and risk profiles are understood, documented, and appropriately applied across different classes of software work, and maintain deep understanding of the strengths, limitations, and risk characteristics of approved LLMs (e.g., Claude, ChatGPT, and successor models), including safety profiles, context limits, determinism strategies, and fine tuning vs. prompt only tradeoffs, design multi agent workflows that incorporate LLM driven analysis, code generation, testing, and review with explicit human approval gates and segregation of duties. Ensure LLM driven systems meet enterprise reliability and resilience expectations, including disaster recovery, fallback behaviors, regional resiliency, and performance SLOs and Drive LLM Ops best practices, integrate models, prompts, and evaluation into CI/CD, enforce approvals, segregation of duties, and reproducibility, automate regression and guardrail tests and manage lifecycle across environments. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Formal training or certification in software engineering concepts, with practical experience of minimum 1 year applying them to LLM enabled systems in regulated environments and Strong understanding of data modeling challenges in big data and LLM contexts, embeddings, chunking strategies, vector similarity nuances, retrieval quality measures, and document lineage. 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 .