Senior Applied AI Scientist

About the Role

We are looking for a Senior Applied AI Scientist to design and develop cutting-edge Generative AI and LLM-powered systems for real-world, high-impact applications.

This is a senior, hands-on technical role for someone who can operate across key components of modern AI — from modeling and experimentation to production systems — while contributing to system design and collaborating across teams.

You will work at the intersection of LLMs, agentic systems, retrieval architectures, and scalable AI platforms, building systems that move from prototypes to reliable, production-grade solutions.

Key Responsibilities

• Design and develop large-scale AI/ML and Generative AI systems
• Build and deploy LLM-powered applications, including RAG pipelines and agent-based systems
• Contribute to the architecture and implementation of scalable AI systems
• Collaborate with scientists, engineers, and product teams to deliver end-to-end AI solutions
• Develop retrieval and embedding systems for intelligent applications
• Implement agentic workflows with tool use, memory, and reasoning capabilities
• Support the full model lifecycle: data curation → training/fine-tuning → evaluation → deployment → monitoring
• Translate business requirements into practical AI solutions in collaboration with stakeholders

Required Qualifications

• 4+ years of experience in Applied AI / Machine Learning / Generative AI
• Strong experience building and deploying production-grade AI systems
• Experience working in cross-functional teams to deliver AI solutions
• Ability to communicate technical concepts effectively with product and engineering stakeholders
• Deep expertise in LLMs, RAG, and modern Generative AI systems
• Solid system design skills across data, models, and infrastructure
• Ability to move from experimentation → production deployment
• Strong ownership mindset and ability to operate in fast-paced environments
• Experience with agentic frameworks and tool-based AI systems

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