Senior Software Engineer – Agent AI
| Company | Red Cell Partners |
|---|---|
| Location | Seattle, WA, USA |
| Salary | $150000 – $180000 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Senior |
Requirements
- Masters/PhD (or Bachelor’s with equivalent experience) in computer science, statistics, mathematics, or related field, or equivalent work experience
- 5+ years of experience in software engineering, specifically building and delivering data science, machine learning, or data analytics solutions in products at scale
- Proficient in one or more programming languages, such as Python, R, or Scala
- Experience with machine learning frameworks and libraries, such as TensorFlow, PyTorch, or Scikit-learn
- Experience with cloud platforms and services, such as AWS, Azure, or GCP
Responsibilities
- Design, Develop, and Deploy Agentic AI: Help build and iterate on a production agent architecture involving LLMs, RAG, and context/memory management; develop additional models to complement and interact with foundational models; understand requirements, design solutions, collaborate with data engineers, and deploy models into production.
- Develop and Manage AI/ML Infrastructure and Lifecycle: Build and maintain scalable and reliable AI/ML infrastructure; contribute to the full AI/ML lifecycle from development to production; implement observability tools to collect data and ensure model performance.
- Collaborate within a Cross-Functional Team: Work closely with product managers, product developers, threat analysts, data scientists, and other engineers to understand business requirements and translate them into ML solutions.
- Keep Pace with Research: Stay updated with the latest research in machine learning, large language models, and cyber security and implement new findings into core technology.
Preferred Qualifications
- Experience training and fine-tuning large-scale foundational models, such as LLMs, or developing other production-grade language models, with a strong understanding of managing AI infrastructure and projects in a DevOps/MLOps setting.
- Familiarity with security concepts, tools, and solutions, such as threat detection, vulnerability scanning, encryption, or authentication