Sr Lead Software Engineer - Agentic Pricing & AI/ML
About The Position
As a Senior Lead Software Engineer within JPMorganChase’s Commercial & Investment Bank, Payments Technology, you will help build the standard platform for developing and running AI agents in production. The platform provides a common runtime and shared capabilities so teams can move agents from idea to trusted live services. As a senior technical authority for the Jersey City team, you will shape the architecture for agentic pricing and for training, serving, and governing pricing and optimization models used as tools by AI agents. Your decisions will support agent-enabled pricing while preserving the accuracy, explainability, security, and auditability required of pricing systems. You will set architecture and engineering standards, develop secure production software, and partner with platform, engineering, pricing, product, finance, and risk teams to create reusable agent and MLOps capabilities. You will also provide technical leadership through architecture evaluations, design reviews, documented decisions, and engineer mentoring. JPMorganChase offers a competitive total rewards package, with base salary determined by role, experience, skill set, and location. Eligible employees may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity. Benefits and programs are provided based on eligibility and include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, and financial coaching. Additional compensation and benefits details are provided during the hiring process.
Requirements
- 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 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 that support complex business processes.
Nice To Haves
- Strong understanding of concurrency and distributed computation, including multithreading and the async model, process and worker architectures, task queues and backpressure, and failure modes such as races, deadlocks, retries, and 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.
- Production experience with optimization, pricing, revenue management, elasticity or econometric models, knowledge graphs, GraphRAG, or organizational memory.
- 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.
Responsibilities
- Set technical direction and reference architecture for agentic pricing, including multi-agent topology, A2A communication, MCP tool integration, and boundaries between LLM reasoning and deterministic pricing engines.
- Design controls for pricing integrity, including versioned and effective-dated pricing logic, audit trails, price explanations, and separation of price proposal and approval.
- Design secure authorization and information barriers for agents operating across clients, sellers, and regions, including fine-grained authorization and isolated execution of sensitive pricing data.
- Define MLOps architecture for models used as agent tools, covering training, environment promotion, model registry, Kubernetes-based serving, monitoring, challenger models, back-testing, and retraining.
- Establish standards for agent quality and safety, including evaluation frameworks, guardrails, observability, and evidence for model risk review.
- Develop creative software solutions through design, development, and technical troubleshooting of complex technical problems.
- Develop secure, high-quality production code for critical systems and review, troubleshoot, and debug code developed by others.
- Partner with platform, engineering, pricing, product, finance, and risk teams to develop reusable capabilities, shared agent and MLOps patterns, and governance for pricing decisions.
- Drive adoption of enterprise-authorized AI-assisted engineering practices and establish validation standards for secure coding, peer review, automated testing, correctness, performance, and security.
- Apply expertise across the Software Development Life Cycle and identify opportunities to automate or eliminate recurring issues to improve application and system stability.
- Provide technical leadership through architecture and technology evaluations, design reviews, documented architecture decisions, and mentoring engineers.
Benefits
- Competitive total rewards package.
- Commission-based pay and/or discretionary incentive compensation for eligible roles, in cash and/or forfeitable equity.
- Comprehensive health care coverage.
- On-site health and wellness centers.
- Retirement savings plan.
- Backup childcare.
- Tuition reimbursement.
- Mental health support.
- Financial coaching.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
