AI/ML Senior Lead Software Engineer - Agentic Pricing

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 Senior Lead Software Engineer at JPMorganChase within the Commercial and Investment bank Digital & Platform Services, Data Analytics, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
• Design and ship production agents on NEO across the federated portfolio, owning them from prototype through production.
• Build retrieval that holds up in production: Graph RAG combining knowledge-graph traversal with vector search, plus chunking, ranking, and grounding strategies that keep answers accurate and auditable.
• Design agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case.
• Own organizational context management - assembling entitlement-, lineage-, and tenant-aware context so each agent reasons over only what it’s allowed to see.
• Compose multi-agent workflows using A2A, and integrate tools and data through MCP servers (Bitbucket, Confluence, Databricks, Kubernetes, Snowflake, Splunk).
• Build and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, and quality safety gating before release.
• Deploy and operate solutions on public cloud (AWS and, or Azure) with strong SDLC, security, resiliency, and observability practices.
• Partner with product and business partners across CIB and Payments to turn use cases into shipped, supported agents.
• Serves as a function-wide subject matter expert in one or more areas of focus
• Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience with at least 4 years working on AI/ML solutions.
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Advanced in one or more programming language(s) - Strong programming skills in Python, with deep knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics.
• Hands-on experience building LLM-powered or agentic applications in production, including tracing, evaluations, and guardrails.
• Deep proficiency with Kubernetes and Amazon EKS, micro-VM isolation (e.g., Firecracker, Kata Containers, gVisor), and sidecar architectures, with proven experience designing defense in depth across the stack: network and mTLS, workload identity, fine-grained authorization, runtime isolation for untrusted or adversarial workloads, and application-level guardrails
• Practical RAG experience - retrieval quality, embeddings, and vector stores; Expert knowledge of at least one of: AWS, Azure, Kubernetes.

  Knowledge of data management and data model design; real-time processing using

  both SQL (e.g., Postgres) and NoSQL stores (e.g., OpenSearch, Redis).

• Excellent communication skills with the ability to partner effectively with senior technical and business stakeholders.
• Proficient in all aspects of the Software Development Life Cycle
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

• In-depth knowledge of the financial services industry and their IT systems

  Preferred qualifications, capabilities, and skills

• MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience).
• Experience with agent frameworks or runtimes, A2A, or MCP. Agent memory design (memory nodes, episodic/semantic memory) and organizational context management.
• Knowledge graphs and graph databases used for retrieval. Graph RAG a strong plus.
• Understanding of LLM fine-tuning and small language model inference.
• Ability to develop full-stack products using modern JavaScript/TypeScript frameworks (e.g., Next.js, Svelte) for agent UIs (AG-UI , NEO UI SDK).
• Experience working in the financial or payments domain at a large institution (Investment Banking, Markets, Securities Services, or adjacent).
• Knowledge of high-performance languages such as Go or Rust