Executive Director Principal Software Engineer - Agentic Engineering
About The Position
As the Principal Software Engineer at JPMorgan Chase within the Corporate and Investment Bank Technology – Securitized Product Group Technology team, you will lead the design, build, and scaling of our next-generation multi-agent AI platform. This is not a manage-from-a-distance role: you will write production code, own key architectural decisions, unblock engineers, and be accountable for production outcomes—while also setting multi-year strategy and building a high-performing team. If you’ve built agentic systems in production (not prototypes), can distinguish orchestration vs. parallelization in real distributed environments, and can whiteboard with a CTO then land a PR the same afternoon—this role is for you.
Requirements
- 10+ years in software engineering, including 5+ years leading senior technical teams and driving architecture for large systems.
- Expert Python; working proficiency in TypeScript, Go, or Rust.
- Deep familiarity with agentic frameworks (LangChain, LangGraph, AutoGen, or equivalent).
- Strong command of RAG, prompt/context design, tool/function calling, and multi-step reasoning patterns.
- Distributed systems and data platform experience: Kafka, Spark, gRPC/REST, Docker/Kubernetes.
- Cloud fluency (AWS/Azure/GCP) and MLOps tooling (MLflow, Kubeflow, SageMaker, Vertex AI).
- Familiarity with vector databases, knowledge graphs, and semantic retrieval patterns.
- Strong executive communication—able to align stakeholders on trade-offs, timelines, and risk.
- Platform mindset: builds durable systems, not fragile demos.
Nice To Haves
- kdb+/q, ClickHouse, or other time-series/analytical data stores.
- Financial services domain (trading infrastructure, derivatives, fixed income).
- Publications, talks, or open-source contributions in agentic AI.
- Experience designing “agentic SDLC” / software-delivery automation.
Responsibilities
- Own the multi-year agentic platform strategy: agent toolchains, RAG pipelines, memory/state architectures, context management, evaluation, and feedback/reinforcement loops.
- Architect scalable multi-agent systems using LangChain, LangGraph, AutoGen, or equivalent frameworks—and define when to use simpler primitives.
- Design distributed ingestion and workflow systems (batch + streaming) with data contracts, lineage, and strong data-quality patterns.
- Establish standards for the agentic development lifecycle: context engineering, automated evals, observability, security, and release readiness.
- Contribute directly in Python (services, concurrency, performance, reliability) and set the bar via reference implementations.
- Lead design and code reviews; engage directly in incident response and production hardening.
- Build reusable agent components: planning/decomposition, tool/function calling, self-critique/reflection loops, state management, multi-agent coordination, and safety controls.
- Partner with MLOps/platform on deployment, monitoring, and retraining pipelines (MLflow, SageMaker, Vertex AI, Azure ML, etc.).
Benefits
- competitive total rewards package including base salary determined based on the role, experience, skill set and location
- commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions
- comprehensive health care coverage
- on-site health and wellness centers
- a 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
Executive
Education Level
No Education Listed
Number of Employees
5,001-10,000 employees
