Full Job Description
We are looking for a Principal Applied AI Scientist to lead the design and deployment of cutting-edge Generative AI and LLM-powered systems for real-world, high-impact applications.
This is a senior, hands-on leadership role for someone who can operate across the full stack of modern AI - from research and modeling to production systems and productization - while leading teams and defining technical direction.
You will work at the intersection of LLMs, agentic systems, retrieval architectures, and large-scale AI platforms, building systems that move beyond prototypes into robust, production-grade intelligence systems.
What You'll Do
- Lead the end-to-end design and development of large-scale AI/ML and Generative AI systems
- Architect and deploy LLM-powered applications, including RAG pipelines and multi-agent systems
- Drive the technical vision and roadmap for applied AI across the company
- Build and lead a high-performing team of scientists and engineers
- Design scalable retrieval and embedding systems powering intelligent applications
- Develop agentic AI systems with tool use, memory, and reasoning capabilities
- Own model lifecycle: data curation training/fine-tuning evaluation deployment monitoring
- Partner with product, engineering, and executive leadership to translate business problems into AI solutions
Required Qualifications
- 7+ years of experience in Applied AI / Machine Learning / Generative AI
- Proven experience building and deploying production-grade AI systems at scale
- Demonstrated leadership managing large cross-functional teams
- Strong experience engaging with executives and product stakeholders
- Deep expertise in LLMs, RAG, and agentic AI systems at scale
- Strong system design skills across data, models, and infrastructure
- Ability to move from research ideas production systems business impact
- Strong ownership mindset with the ability to operate in fast-moving startup environments
- Experience with multi-agent orchestration frameworks and tool ecosystems
Preferred Qualifications
- Experience applying AI in cybersecurity, enterprise SaaS, or data-intensive domains
- Background in search or large-scale retrieval systems
Core Technical Expertise
- Large Language Models & GenAI
- RAG, Retrieval & Vector Systems
- Fine-Tuning & Model Adaptation
- Agentic AI Systems
- Prompt Engineering & Optimization
- Evaluation & Quality
- Inference & Serving
- Data Engineering & Synthetic Data
- Multimodal AI
- LLMOps & Observability
- Platforms & Infrastructure
