SR Principal Software Engineer - LLM Engineering

We're looking for a tech leader ready to take their career to new heights. Join the ranks of top talent at one of the world's most influential companies.

As a Senior Principal Software Engineer at JPMorgan

Chase within the Commercial & Investment Bank Trust & Safety Fraud Prevention team, you provide deep engineering expertise and work across agile teams to enhance, build, and deliver trusted market‑leading technology products in a secure, stable, and scalable way. Leverage your deep expertise to consistently challenge the status quo, innovate for business impact, lead the strategic development behind new and existing products and technology portfolios, and remain at the forefront of industry trends, best practices, and technological advances.

• Advises and leads on the strategy, architecture, and development of model serving solutions for different model architectures (including LLMs & GNNs) across cloud and on‑premises environments, aligning initiatives to business outcomes.
• Defines and implements MLOps and LLMOps strategies for end‑to‑end model lifecycle management, including training, versioning, deployment, monitoring, and governance.
• Drives optimization of model inferencing for high throughput and low latency using quantization, model parallelism, intelligent batching, and hardware acceleration for all model architectures.
• Sets strategy and operating standards for agentic AI-enabled engineering across a portfolio (using enterprise-authorized tools within the work environment) to drive measurable improvements in delivery speed, reliability, and code quality (e.g., AI-orchestrated SDLC/TLM automation, release readiness gating, incident triage/root‑cause acceleration, and large‑scale refactoring/test modernization), while defining guardrails for validation, security, resiliency, and reuse across teams and functions.
• 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.
• Creates durable, reusable software and platform frameworks to standardize ML Engineering services, enabling scale across teams and functions.
• Establishes best practices for automation, CI/CD, and infrastructure‑as‑code using containerization and orchestration technologies.
• Partners closely with data science, platform engineering, and SRE teams to product ionize models on AWS, ensuring observability, reliability, and cost efficiency.
• Leads deployment and optimization using model inference servers such as Triton Inference Server and vLLM for high‑throughput, low‑latency serving at scale.
• Oversees production operations for AI workloads, including monitoring, incident response, security, and compliance, with continuous improvement.
• Translates complex technical concepts and emerging trends into actionable strategies, influencing senior stakeholders and cross‑functional partners to prioritize and deliver AI/ML capabilities that drive measurable business impact while promoting a culture of diversity, opportunity, inclusion, and respect.

• Required qualifications, capabilities, and skills
  Formal training or certification on software engineering concepts and 10+ years of applied experience.
• 8+ years of AI/ML engineering experience with significant expertise in LLMs, GNNs and other model architectures (e.g., GPT, Llama, Falcon, Mistral).
• Demonstrated success architecting and deploying LLM & GNN solutions on AWS (e.g., Sage Maker, Bedrock, EKS) at enterprise scale; experience with Azure ML or GCP Vertex AI.
• Experience building LLM and GNN serving platforms in large‑scale environments typical of major tech firms.
• Hands‑on experience building LLM inference engines using Triton Inference Server and vLLM, including autoscaling, caching, and throughput optimization.
• Advanced proficiency in Python and optimization techniques applied to deep learning frameworks (PyTorch, Tensor Flow, Hugging Face Transformers).
• Deep understanding of LLMOps/MLOps (e.g., MLflow, Sage Maker Pipelines, Kubeflow) with a track record of implementing best practices at scale.
• Demonstrated experience…