Principal AI/ML Engineer
Posted Aug 25, 2026
About the role
We are seeking a visionary Principal AI/ML Engineer to lead the development and optimization of advanced machine learning models in natural language processing, computer vision, and speech processing. In this role, you will architect, implement, and fine-tune Large Language Models (LLMs) and diffusion models, shaping the future of our comprehensive data analytics platform. You will be responsible for building end-to-end ML pipelines, implementing state-of-the-art transformer architectures, and deploying scalable models into production. This position requires a strong research mindset to integrate cutting-edge techniques and a collaborative spirit to mentor team members. The ideal candidate is a driven expert in deep learning who thrives on solving complex challenges and is passionate about making a global impact through innovative AI solutions.
Requirements
- Advanced degree (Master's or PhD) in Computer Science, Machine Learning, or a related field.
- 4+ years of professional experience in machine learning engineering with a deep focus on deep learning and neural networks.
- Expert-level Python skills and extensive experience with PyTorch, TensorFlow, and the HuggingFace ecosystem.
- Demonstrated expertise in designing, implementing, and fine-tuning Large Language Models (e.g., GPT, BERT, LLaMA).
- Hands-on experience with diffusion models (e.g., Stable Diffusion, DDPM) for generative tasks.
- Proven experience with ASR systems (e.g., Whisper, Wav2Vec2) and building speech processing pipelines.
- Solid understanding of parameter-efficient fine-tuning (PEFT) methods such as LoRA and QLoRA.
- Familiarity with MLOps practices and tools (Docker, Kubernetes, MLflow) for model deployment and monitoring.
Desirable skills
- Experience with multimodal learning, combining text, audio, and visual data.
- Knowledge of Reinforcement Learning from Human Feedback (RLHF).
- Experience developing real-time inference systems for streaming data.
- Familiarity with model optimization techniques like quantization, pruning, or TensorRT.
- Contributions to open-source ML projects or publications in top-tier AI conferences.