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About the role
Role Overview: You will work across the full AI lifecycle - research, experimentation, system design, and production deployment. This role is ideal for someone who has worked with modern LLM-based systems, understands both research and applied AI, and is excited to build scalable, real-world AI solutions. You will collaborate closely with founders, engineers, and product teams to design intelligent systems, push the boundaries of applied AI, and turn cutting-edge research into business value. Key Responsibilities: - Conduct research on advanced AI techniques, including LLMs, RAG pipelines, multimodal models, and agentic workflows. Stay up to date with the latest research papers, tools, and frameworks, and translate them into practical applications. Design experiments to evaluate new architectures, prompting strategies, fine-tuning methods, and retrieval techniques. - Build and optimize LLM-powered systems such as RAG, tool-using agents, and multi-step reasoning pipelines. Work with vector databases, embeddings, retrieval strategies, and memory systems. Improve model performance through prompt engineering, fine-tuning, evaluation frameworks, and feedback loops. - Develop, train, and optimize machine learning and deep learning models for NLP, vision, speech, or multimodal tasks. Handle data pipelines including preprocessing, labeling strategies, and quality analysis. Evaluate models using both quantitative metrics and qualitative analysis. - Collaborate with engineering teams to deploy models into production environments. Build scalable prototypes and internal tools to validate research ideas quickly. Optimize AI systems for latency, cost, reliability, and real-world constraints. - Design rigorous experiments and ablation studies to guide technical decisions. Analyze results, identify failure modes, and iteratively improve system performance. Contribute to internal benchmarks and evaluation frameworks. - Maintain clear documentation of research findings, system designs, and experiments. Present insights and demos to technical and non-technical stakeholders. Mentor junior researchers or engineers as the team grows. Qualifications: - Strong background in Computer Science, AI, ML, or a related field (degree or equivalent practical experience). - Proven experience working with LLMs, RAG systems, or modern NLP pipelines. - Solid understanding of machine learning and deep learning fundamentals. - Hands-on experience with frameworks such as PyTorch, TensorFlow, Hugging Face, or similar. - Strong proficiency in Python and experience building production-grade ML systems. - Experience with vector databases, embeddings, evaluation methods, or agent frameworks is a big plus. - Excellent problem-solving ability, intellectual curiosity, and a strong growth mindset. - Ability to think both like a researcher and a product engineer. Role Overview: You will work across the full AI lifecycle - research, experimentation, system design, and production deployment. This role is ideal for someone who has worked with modern LLM-based systems, understands both research and applied AI, and is excited to build scalable, real-world AI solutions. You will collaborate closely with founders, engineers, and product teams to design intelligent systems, push the boundaries of applied AI, and turn cutting-edge research into business value. Key Responsibilities: - Conduct research on advanced AI techniques, including LLMs, RAG pipelines, multimodal models, and agentic workflows. Stay up to date with the latest research papers, tools, and frameworks, and translate them into practical applications. Design experiments to evaluate new architectures, prompting strategies, fine-tuning methods, and retrieval techniques. - Build and optimize LLM-powered systems such as RAG, tool-using agents, and multi-step reasoning pipelines. Work with vector databases, embeddings, retrieval strategies, and memory systems. Improve model performance through prompt engineering, fine-tuning, evaluation frameworks, and feedback loops. - Develop, train, and optimize machine learning and deep learning models for NLP, vision, speech, or multimodal tasks. Handle data pipelines including preprocessing, labeling strategies, and quality analysis. Evaluate models using both quantitative metrics and qualitative analysis. - Collaborate with engineering teams to deploy models into production environments. Build scalable prototypes and internal tools to validate research ideas quickly. Optimize AI systems for latency, cost, reliability, and real-world constraints. - Design rigorous experiments and ablation studies to guide technical decisions. Analyze results, identify failure modes, and iteratively improve system performance. Contribute to internal benchmarks and evaluation frameworks. - Maintain clear documentation of research findings, system designs, and experiments. Present insights and demos to technical and non-technical stakeholders. Mentor junior researchers or engineers