AI Engineer / Senior Data Scientist

Bitsight is a cyber risk management leader transforming how companies manage exposure, performance, and risk for themselves and their third parties. Companies rely on Bitsight to prioritize their cybersecurity investments, build greater trust within their ecosystem, and reduce their chances of financial loss.

Built on over a decade of technological innovation, its integrated solutions deliver value across enterprise security performance, digital supply chains, cyber insurance, and data analysis.

• We invented the cyber ratings industry in 2011
• Over 3000 customers trust Bitsight
• Over 750 teammates are dispersed throughout Boston, Raleigh, New York, Lisbon, Singapore, and remote

We’re looking for an AI Engineer / Senior Data Scientist to take ideas from exploration all the way to production. This is an end-to-end role, combining research, experimentation, and hands-on implementation, with clear ownership and real production impact.

You’ll work with large-scale, highly unstructured data and build LLM- and RAG-based systems that search, organize, and reason over complex information. You’ll partner closely with engineering to ship reliable, scalable solutions used in real-world cyber intelligence workflows.

Responsibilities:

• Design, build, and deploy end-to-end LLM pipelines, including RAG, agentic solutions, and MCP servers
• Take prototypes and experiments into production, ensuring scalability, reliability, and maintainability
• Work with large, dynamic, and unstructured datasets from the deep and dark web
• Develop and maintain classic ML solutions alongside LLM-based systems
• Build evaluation frameworks to measure model quality, performance, and robustness
• Collaborate cross-functionally to define requirements, success metrics, and integration points

Requirements:

• 4+ years of industry experience in data science, applied ML, or AI engineering, with proven production experience
• Strong programming skills in Python, with hands-on work in production environments
• Practical experience working with LLMs, including prompt engineering, evaluation, and RAG techniques
• Experience with LLM orchestration frameworks (e.g., LangChain) and agent-based systems
• Familiarity with modern software development practices (Git, Docker) and data systems such as MongoDB and Elasticsearch
• Strong communication skills and comfort working closely with engineering teams
• A pragmatic mindset — able to turn complex ideas into working systems

Advantage:

• Master’s or PhD in a quantitative field
• Experience fine-tuning and deploying LLMs using tools such as Hugging Face, vLLM, or SageMaker
• Interest in cybersecurity and applying AI to real-world security and risk problems
• Experience sharing knowledge through presentations, internal talks, or mentoring