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Lead Software Engineer - Full Stack/AI Engineering Focus

11d ago
J

JPMorganChase

As a Lead Software Engineer at JPMorganChase, you will be an integral part of an agile team focused on enhancing, building, and delivering trusted, market-leading technology products in a secure, stable, and scalable manner. You will be a core technical contributor responsible for critical technology solutions across multiple technical areas within various business functions to support the firm's business objectives. This role involves designing, developing, and delivering scalable, secure, and resilient full-stack solutions, owning the end-to-end delivery lifecycle for strategic platform capabilities, and leading the design and development of cloud-native, containerized microservices. You will also drive technical architecture and design decisions, build high-performance backend services and APIs, develop modern operational and administrative user interfaces, and design and implement event-driven and streaming architectures. Additionally, you will champion engineering excellence through code reviews and mentoring, lead initiatives to improve application reliability and observability, and leverage enterprise-approved AI-assisted engineering tools to accelerate delivery and enhance productivity. A key aspect of this role is evaluating, designing, and implementing AI-powered and agentic solutions to optimize software development, operational workflows, and engineering processes.

Plano, TX, United States onsite Full-time
PythonTypeScriptJava +5 more

FAQs

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What to know about the AI and engineering roles listed here.

What counts as an AI Engineering job?

AiRagJobs lists engineering roles that build, deploy, or operate AI/ML systems — including LLM engineering, RAG (retrieval-augmented generation), AI agents, prompt engineering, AI infrastructure and MLOps, model serving/inference, and fine-tuning. It excludes non-engineering functions like AI sales or marketing roles.

What is the difference between an AI Engineer and a Machine Learning Engineer?

An AI Engineer typically builds applications on top of existing models — LLM integration, RAG pipelines, agent orchestration, and prompt design. A Machine Learning Engineer more often trains, fine-tunes, or productionizes custom models. Many companies use the titles interchangeably, so search both when browsing.

Does AiRagJobs include AI infrastructure and MLOps roles?

Yes. Titles such as AI Infrastructure Engineer, ML Platform Engineer, GPU Infrastructure Engineer, Inference Engineer, MLOps Engineer, and AI Site Reliability Engineer are all covered — these roles focus on the systems that serve and scale AI models rather than building models themselves.

Are remote AI and ML jobs available?

Yes. Filter by "Remote" on the jobs page to see fully remote AI/ML engineering roles, or browse the remote jobs feed directly.

What skills are most in demand for AI engineering roles?

The most commonly requested skills are LLM APIs (OpenAI, Anthropic, Gemini), RAG and vector databases (Pinecone, Weaviate, Qdrant), agent frameworks (LangGraph, CrewAI, AutoGen), inference/serving tools (vLLM, Ray Serve, TensorRT-LLM), and fine-tuning. Use the skill filters on the jobs page to browse by specific technology.

How fresh are the job listings?

Listings are sourced continuously from company career pages and refreshed automatically. Each job shows when it was posted, and the sitemap and API expose last-updated timestamps for every posting.

How much do AI Engineers get paid?

Compensation varies widely by role, seniority, and location. Where employers disclose a salary range, it is shown directly on the job listing — filter by salary range on the jobs page to narrow results to your target compensation.