Senior AI Foundations Engineer — LLM Tuning & RL

COMFORT SYSTEMS; Cambridge (MA); On-site; USD 230,000 - 262,000; Full time

Job summary

Capital One in Cambridge, MA seeks a Sr. Lead AI Engineer to design and deploy AI-powered systems. You will partner with engineers, researchers, PMs, and product teams to deliver scalable AI solutions that improve customer experiences and associate workflows.

The ideal candidate has deep software engineering and AI/ML expertise, including LLM optimization, vector databases, guardrails, and production-grade deployment.

Qualifications

• Bachelor's degree or higher in a relevant field with significant AI/ML experience.
• 6+ years coding with Python/Go/Scala/Java.
• Experience deploying scalable and responsible AI solutions on cloud platforms.
• Ability to lead and mentor an engineering team.
• Strong communication and problem-solving skills.

Responsibilities

• Partner with cross-functional teams to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components.
• Leverage Open Source and SaaS AI technologies (AWS, HuggingFace, VectorDBs, PyTorch).
• Invent and introduce LLM optimization techniques to improve performance.
• Contribute to long-term AI system roadmap.

Skills

Python

Go

Scala

Java

C++

C#

LLM Inference

Similarity Search

VectorDBs

Guardrails

Memory

PyTorch

HuggingFace

Education

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields

Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields

Tools

AWS

Google Cloud

Azure

HuggingFace

VectorDBs

Nemo Guardrails

PyTorch

Docker

Job description

Capital One in Cambridge, MA seeks a Sr. Lead AI Engineer to design and deploy AI-powered systems. You will partner with engineers, researchers, PMs, and product teams to deliver scalable AI solutions that improve customer experiences and associate workflows.

The ideal candidate has deep software engineering and AI/ML expertise, including LLM optimization, vector databases, guardrails, and production-grade deployment.