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Principal Software Engineer - AI Engineer

JPM*** · Jersey City, NJ, United States
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Jersey City, NJ, United States204,250-285,000 USD/yearlyOnsite
Remuneration
204,250-285,000 USD/yearly
Location
Jersey City, NJ, United States
Visa sponsorship
Sponsors visa

Job summary

JOB DESCRIPTION If you are looking for a game-changing career, working for one of the world's leading financial institutions, you've come to the right place. As a Principal Software Engineer at JPM*** within the Corporate Sector – AI/ML & Data Platforms for LLM Suite, you will lead a specialized technical area, driving impact across teams, technologies, and projects.

Benefits

And programs to meet employee needs, based on eligibility.Additional details about total compensation andWill be provided during the hiring process.We recognize that our people are our strength and the diverse talents they bringWe are an equal opportunity employer and place a high value on diversity and incVisit our FAQs for more information about requesting an accommodation.JPMorgan Chase & Co.Is an Equal Opportunity Employer, including Disability/VeteransABOUT THE TEAMOur professionals in our Corporate Functions cover a diverse range of areas from

Qualifications

  • If you are looking for a game-changing career, working for one of the world's leading financial institutions, you've come to the right place.
  • Capabilities, and

Responsibilities

  • As a Principal Software Engineer at JPM*** within the Corporate Sector – AI/ML & Data Platforms for LLM Suite, you will lead a specialized technical area, driving impact across teams,
  • Design and implement agentic AI reference architectures, including orchestration, retrieval, memory, guardrails, and evaluation harnesses.
  • Write production-quality Python code (PyTorch or TensorFlow as needed) and review critical-path code
  • Create reusable components for prompt management, evaluators, safety filters, connectors, embeddings pipelines, and memory stores
  • Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows
  • Own the end-to-end ML lifecycle: experimentation, CI/CD, automated testing, monitoring, drift detection, versioning, and rollback
  • Optimize inference for latency, throughput, caching, batching, model selection, and cost per inference
  • Partner with data teams on structured and unstructured data pipelines, document ingestion, metadata, and access controls
  • Set engineering standards for agentic AI systems and lead design reviews
  • Influence roadmap and priorities through technical insight and delivery
  • Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized

Skills

Communication

Degrees

Associate

Industry

AutomotiveBankingEnergyGamingHealthcareInsurancePublic-sector

Company size

EnterpriseSmb

Contract length

00 years