Job Description
We are seeking a visionary Senior AI Engineer to architect the artificial intelligence systems of tomorrow. As we look ahead to the transformative technologies of 2026, we are moving beyond static models into the era of autonomous agents, multimodal reasoning, and self-improving systems. This is a rare opportunity to shape the future of AI at a company at the forefront of the industry.
In this role, you will lead the development of next-generation Large Language Models (LLMs) and Generative AI frameworks, ensuring they are scalable, efficient, and ethically sound.
Responsibilities
- Architect LLM Pipelines: Design and implement scalable, high-performance infrastructure for training and deploying large-scale transformer models.
- Research & Innovation: Explore cutting-edge research areas including Chain-of-Thought reasoning, tool use, and agent-based workflows to push the boundaries of current AI capabilities.
- Optimization: Reduce inference latency and operational costs through model quantization, pruning, and efficient serving architectures (e.g., vLLM, TGI).
- Collaboration: Partner with product and engineering teams to define AI roadmaps and integrate advanced AI features into consumer-facing applications.
- RAG & Data: Develop robust Retrieval-Augmented Generation (RAG) systems to enhance factual accuracy and reduce hallucinations.
- Mentorship: Guide a team of talented machine learning engineers and data scientists, fostering a culture of technical excellence and continuous learning.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 5+ years of professional experience in Machine Learning and Deep Learning, with at least 2 years focused on NLP and LLMs.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and Hugging Face Transformers.
- System Design: Deep understanding of distributed systems, cloud architecture (AWS/GCP), and MLOps practices (Kubernetes, Docker, MLflow).
- Research: Track record of publishing in top-tier conferences (NeurIPS, ICML, ACL) or contributing to open-source ML communities.
- Communication: Exceptional ability to translate complex technical concepts into clear business value for stakeholders.