Overview
Skills
Job Details
AI Engineer (Remote) P.HD Candidates Only
Job Summary
We are looking for a highly motivated and skilled Data Scientist with a strong background in deep learning, natural language processing (NLP) and/or computer vision to join our team. In this role, you will be responsible for development/deployment of both computer vision and vision language models (VLMs), while also contributing to the design and implementation of a cutting-edge multi-agent LLM system.
Key Responsibilities:
Develop and optimize advanced computer vision models for a range of real-world applications.
Fine-tune and deploy large language models (LLMs) using domain-specific datasets.
Design and implement scalable frameworks for multi-agent LLM collaboration and interaction.
Work cross-functionally with research, product, and engineering teams to deliver robust, ML-powered solutions.
Conduct rigorous experiments, analyze performance metrics, and iterate on model improvements.
Stay informed on the latest advancements in AI, particularly in vision, NLP, and agent-based systems.
Qualifications:
- Master s or Ph.D. in Computer Science, Machine Learning, or a related field; Ph.D. preferred.
- Extensive hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
- Demonstrated expertise in building and training computer vision models (e.g., CNNs, Vision Transformers).
- Practical experience fine-tuning and deploying large language models (e.g., GPT, DeepSeek, LLaMA, Mistral).
- Familiarity with multi-agent systems, reinforcement learning, or AI planning is a strong plus.
- Proficient in Python with experience in designing machine learning pipelines and working with cloud-based platforms. Hands-on experience with deploying ML systems on AWS using tools like AWS CDK, Docker, and related DevOps practices.
Strong analytical and problem-solving skills with the ability to work independently and in collaborative environments.
Preferred Qualifications:
Strong understanding of vision-language models and transformer-based architectures.
Proven experience training state-of-the-art VLMs (e.g., CLIP, BLIP, Flamingo, LLaVA) on both domain-specific and large-scale datasets.
Familiarity with multi-agent systems architecture and design principles.
Experience deploying machine learning models in cloud environments, particularly using AWS services (e.g., SageMaker, EC2, Lambda).