Sr Lead Software Engineer - Agentic Pricing & AI/ML

As a Senior Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank, Payments Technology, you will be a part of a team creating its standard platform for building and running AI agents in production . It gives teams a co mmon runtime and the shared capabilities they need to take an agent from idea to a live, trusted service, so they can focus on what the agent does rather than rebuilding every time. As a senior technical authority for the Jersey City team, you will help set the architecture for our agentic pricing capabilities and how pricing and optimization models are trained, served, and governed as tools used by AI agents. Your technical decisions will help enable agent-supported pricing while maintaining the accuracy, explainability, security, and auditability required of our pricing systems.

Job Responsibilities

- Sets the technical direction and reference architecture for agentic pricing, including multi-agent topology, agent-to-agent (A2A) communication, Model Context Protocol (MCP) tool integration, and the boundaries between large language model (LLM) reasoning and deterministic pricing engines
- Designs control architecture for pricing integrity, including versioned and effective-dated pricing logic, comprehensive audit trails, price explanations, and appropriate segregation between proposing and approving prices
- Designs secure authorization and information barriers for agents operating across clients, sellers, and regions, including fine-grained authorization and isolated execution of sensitive pricing data
- Defines MLOps architecture for models used as agent tools, including model training, environment promotion, model registry, Kubernetes-based serving, monitoring, challenger models, back-testing, and retraining practices
- Establishes engineering standards for agent quality and safety, including evaluation frameworks, guardrails, observability, and evidence supporting appropriate model risk review
- Executes creative software solutions, design, development, and technical troubleshooting, applying innovative approaches to solve complex technical problems
- Develops secure, high-quality production code for critical systems and reviews, troubleshoots, and debugs code developed by others
- Partners with platform, engineering, pricing, product, finance, and risk teams to develop reusable capabilities, establish shared agent and MLOps patterns, and support effective governance of pricing decisions
- Drives adoption of enterprise-authorized AI-assisted engineering practices while establishing validation standards for secure coding, peer review, automated testing, correctness, performance, and security
- Applies expertise across the Software Development Life Cycle and identifies opportunities to automate or eliminate recurring issues to improve application and system stability
- Provides technical leadership through architecture and technology evaluations, design reviews, documented architecture decisions, and mentoring of engineers

Required Qualifications, Capabilities, and Skills

- Formal training or certification in software engineering concepts and 10+ years of applied experience
- Hands-on experience delivering system design, application development, testing, technical troubleshooting, and operational stability
- Advanced proficiency in one or more programming languages, including Python
- Proven experience architecting and delivering production LLM agents or multi-agent systems, including tool use, memory, evaluation, and guardrails
- Proven experience designing end-to-end machine learning platforms or MLOps pipelines spanning model training, deployment, serving, monitoring, and governance
- Experience architecting systems requiring deterministic, explainable, and auditable outputs, such as pricing, billing, valuation, or risk systems
- Experience designing secure distributed systems, including identity, authorization, service-to-service trust, and controls for sensitive data
- Demonstrated experience applying AI-assisted software development tools and establishing appropriate validation of AI-generated outputs for correctness, performance, security, and resiliency
- Proficiency across the Software Development Life Cycle, including agile methodologies, CI/CD, application resiliency, security, and operational practices
- Practical cloud-native experience, including Kubernetes and Amazon EKS, with knowledge of workload identity, network security, fine-grained authorization, runtime isolation, and application-level controls
- Knowledge of financial services technology environments and experience designing secure, resilient systems supporting complex business processes

Preferred Qualifications, Capabilities, and Skills

- Strong understanding of concurrency and distributed computation: multithreading and the async model, process and worker architectures, task queues and backpressure, and the failure modes of concurrent distributed systems (races, deadlocks, retries, idempotency)
- Experience with agent protocols and frameworks such as MCP, A2A, AG-UI, Google ADK, or LangGraph
- Experience with regulated workflows in which LLM outputs must be constrained, attributable, explainable, and auditable
- Experience with authorization, service-mesh, or workload-isolation technologies such as OpenFGA, OPA/Rego, Istio/Envoy mTLS, Kata Containers, gVisor, or Firecracker
- Experience with Databricks, MLflow, Kubernetes-based model serving, GPU capacity planning, or small language model inference
- Experience with optimization, pricing, revenue management, elasticity or econometric models, knowledge graphs, GraphRAG, or organizational memory in a production environment
- Experience with model risk management or comparable governance processes for models influencing revenue or client pricing, and/or knowledge of commercial card, treasury services, merchant pricing, billing, or revenue systems