S
AI Engineer
Stable10
5 hours ago
Full-time
On-site
AI Engineer (LLM & Generative AI)
Location: US East Coast (Remote)
Experience: 6-12 Years
We’re partnering with a fast-growing venture-backed AI startup that’s building and deploying production-grade AI solutions for large enterprise customers. They are looking for an experienced AI Engineer to lead the implementation and deployment of a custom AI platform for a major financial services organization.
This is a hands‑on, high‑impact role for someone who thrives in startup environments, enjoys solving complex technical challenges, and has experience taking AI products from concept to production.
What You’ll Do
Lead the end-to-end implementation of a customized AI/LLM platform within a large enterprise environment
Design, build, and deploy production‑grade AI applications and services
Collaborate closely with a globally distributed engineering team to integrate customer‑specific functionality into the core platform
Own system reliability, performance, and scalability across AI‑powered workflows
Act as a technical partner to customer engineering and technology stakeholders
Troubleshoot complex production issues and drive solutions independently
Contribute to architecture decisions, platform evolution, and engineering best practices
Tech Stack
Python
Large Language Models (LLMs)
Generative AI
Retrieval‑Augmented Generation (RAG)
Agentic AI Systems
Kubernetes
CI/CD Pipelines
REST APIs
AWS, GCP, or Azure
Required Experience
6-12 years of software engineering experience, with a strong focus on backend or AI engineering
Proven experience building and deploying LLM‑based or agentic AI applications in production
Strong Python development skills and experience delivering customer‑facing product features end-to-end
Deep understanding of software architecture, distributed systems, and system design
Experience working directly with enterprise customers and shipping production software
Bachelor’s degree in Computer Science or a related technical field
Ideal Background
Currently or recently working at a high‑growth, venture‑backed AI startup (Series A-D)
Experience building AI products used by external customers rather than internal‑only tools
Comfortable operating in fast‑paced, ownership‑driven environments
Strong communication skills and ability to work directly with technical stakeholders
Nice to Have
Experience with cloud infrastructure (AWS, GCP, or Azure)
Kubernetes and containerized deployments
CI/CD automation and observability tooling
Experience operating AI systems in regulated or enterprise environments
Not the Right Fit If
Your experience is primarily research-focused with limited production deployment ownership
You’ve mainly worked as a Data Scientist, Data Engineer, ML Researcher, or DevOps/SRE without significant software engineering responsibility
You’re looking for a highly structured large‑enterprise environment rather than a startup setting
Why Join?
Build real AI products being used in production today
Work directly with enterprise customers solving high‑value problems
High ownership and autonomy from day one
Opportunity to shape the future of an AI platform at a rapidly growing startup
Collaborative, execution‑focused engineering culture
Interested? We’d love to hear from engineers who have successfully taken LLM‑powered products from prototype to production and enjoy owning technical outcomes end-to-end.
#J-18808-Ljbffr
This is a hands‑on, high‑impact role for someone who thrives in startup environments, enjoys solving complex technical challenges, and has experience taking AI products from concept to production.
What You’ll Do
Lead the end-to-end implementation of a customized AI/LLM platform within a large enterprise environment
Design, build, and deploy production‑grade AI applications and services
Collaborate closely with a globally distributed engineering team to integrate customer‑specific functionality into the core platform
Own system reliability, performance, and scalability across AI‑powered workflows
Act as a technical partner to customer engineering and technology stakeholders
Troubleshoot complex production issues and drive solutions independently
Contribute to architecture decisions, platform evolution, and engineering best practices
Tech Stack
Python
Large Language Models (LLMs)
Generative AI
Retrieval‑Augmented Generation (RAG)
Agentic AI Systems
Kubernetes
CI/CD Pipelines
REST APIs
AWS, GCP, or Azure
Required Experience
6-12 years of software engineering experience, with a strong focus on backend or AI engineering
Proven experience building and deploying LLM‑based or agentic AI applications in production
Strong Python development skills and experience delivering customer‑facing product features end-to-end
Deep understanding of software architecture, distributed systems, and system design
Experience working directly with enterprise customers and shipping production software
Bachelor’s degree in Computer Science or a related technical field
Ideal Background
Currently or recently working at a high‑growth, venture‑backed AI startup (Series A-D)
Experience building AI products used by external customers rather than internal‑only tools
Comfortable operating in fast‑paced, ownership‑driven environments
Strong communication skills and ability to work directly with technical stakeholders
Nice to Have
Experience with cloud infrastructure (AWS, GCP, or Azure)
Kubernetes and containerized deployments
CI/CD automation and observability tooling
Experience operating AI systems in regulated or enterprise environments
Not the Right Fit If
Your experience is primarily research-focused with limited production deployment ownership
You’ve mainly worked as a Data Scientist, Data Engineer, ML Researcher, or DevOps/SRE without significant software engineering responsibility
You’re looking for a highly structured large‑enterprise environment rather than a startup setting
Why Join?
Build real AI products being used in production today
Work directly with enterprise customers solving high‑value problems
High ownership and autonomy from day one
Opportunity to shape the future of an AI platform at a rapidly growing startup
Collaborative, execution‑focused engineering culture
Interested? We’d love to hear from engineers who have successfully taken LLM‑powered products from prototype to production and enjoy owning technical outcomes end-to-end.
#J-18808-Ljbffr