Principal Software Engineer - AI Engineer

JPMorganChase•Jersey City, NJ

About The Position

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 JPMorganChase 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. In this role, you will leverage your deep knowledge of machine learning, software engineering, and product management to spearhead multiple complex ML projects and initiatives, serving as the primary decision-maker and a catalyst for innovation and solution delivery. LLM Suite is JPMorganChase’s premier internally built AI tool leveraged by +250k employees for everything from individual productivity to larger scale, business solutions. You will be responsible for hiring, leading, and mentoring a team of Machine Learning and Software Engineers, focusing on best practices in ML engineering, with the goal of elevating team performance to produce high-quality, scalable ML solutions with operational excellence.

Requirements

Formal training or certification on software engineering concepts and 7+ years applied experience
Strong Python engineering skills; experience with PyTorch or TensorFlow
Expertise working with Vector storage systems and designing memory for Agents
Expertise developing long running agents that run autonomously using tools, skills and human in the loop
Proven experience deploying LLM-backed services to production (APIs, microservices)
Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance
Cloud-native AI deployment experience (AWS or Azure), with cost and performance optimization
Demonstrated commitment to responsible AI practices and operational excellence
Strong communication and collaboration skills, working across product, risk, legal, and compliance teams
Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data.
Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.

Nice To Haves

Experience with fine-tuning, adapters, or custom evaluation frameworks
Background operating AI systems in regulated environments (finance, healthcare, etc.)
Experience with prompt engineering and LLM orchestration
Knowledge of safety filters, audit logging, and explainability in production systems
Experience mentoring senior engineers and leading architecture discussions
Demonstrated ability to influence technical roadmaps and priorities

Responsibilities

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 tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale.