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About This Role
JOB DESCRIPTION
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 JPMorganChase 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.
Job responsibilities
- 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
- 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 productionize the 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 highly complex technical concepts and emerging trends into actionable strategies for executive and product leadership.
- Influences senior stakeholders and cross‑functional partners to prioritize and deliver AI/ML capabilities that drive measurable business impact.
- Promotes the firm’s culture of diversity, opportunity, inclusion, and respect across teams and communities.
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., SageMaker, Bedrock, EKS) at enterprise scale; experience with Azure ML or GCP Vertex AI.
- Experience building LLM, 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, TensorFlow, Hugging Face Transformers).
- Deep understanding of LLMOps/MLOps (e.g., MLflow, SageMaker Pipelines, Kubeflow) with a track record of implementing best practices at scale.
- Expertise in inference optimization and distributed systems for large models focused on high‑throughput, low‑latency applications.
- Practical experience delivering system design, application development, testing, and operational stability for enterprise AI platforms.
- Proven collaboration with SRE to implement observability, incident response, and SLIs/SLOs for LLM services.
- Excellent communication skills with the ability to influence both technical and non‑technical stakeholders and deliver value across functions at scale.
Preferred qualifications, capabilities, and skills
- Master’s or PhD in Computer Science, Engineering, or a related field (or equivalent experience).
- Practical cloud‑native experience, including containerization (Docker), orchestration (Kubernetes), and infrastructure‑as‑code (Terraform, CloudFormation).
- Expertise in security, compliance, and governance for AI/ML deployments in regulated environments.
- Experience in trust and safety or fraud prevention domains; familiarity with payments platforms is a plus.
- Track record of contributions to open‑source LLM projects or peer‑reviewed research and/or experience presenting at industry conferences or leading technical communities.
- Familiarity with hardware acceleration strategies across GPUs, TPUs, and specialized inference runtimes.
- Experience in building java based applications
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.
ABOUT US
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive 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. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on\-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase \& Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
J.P. Morgan’s Commercial \& Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial \& Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.
Role Details
About This Role
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 26,159 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At JPMorganChase, this role fits into their broader AI and engineering organization.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
Skills Required
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Compensation Benchmarks
LLM Engineer roles pay a median of $285,250 based on 4 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $232K to $325K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
JPMorganChase AI Hiring
JPMorganChase has 192 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist, AI Agent Developer. Positions span Columbus, OH, US, New York, NY, US, San Francisco, CA, US. Compensation range: $62K - $500K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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