Develop machine learning and deep learning solutions, and experimenting with state of the art models to drive the future of machine learning at AI Technologies.
Requirements
- At least 5 year's experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must.
- At least 5 years’ experience in applying data science, ML techniques to solve business problems.
- Solid background in Natural Language Processing (NLP) and Large Language Models (LLMs)
- Experience with machine learning and deep learning methods.
- Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow.
- Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search).
- Experience with Ray, MLFlow, and/or other distributed training frameworks.
Responsibilities
- Serve as a subject matter expert on a wide range of ML techniques and optimizations.
- Provide in-depth knowledge of ML algorithms, frameworks, and techniques.
- Enhance ML workflows through advanced proficiency in large language models (LLMs) and related techniques.
- Conducting experiments using latest ML technologies, analyzing results, tuning models
- Hands on coding to bring the experimental results into production solutions by collaborating with engineering team. Owning end to end code development in python for both proof of concept/experimentation and production-ready solutions.
- Optimizing system accuracy and performance by identifying and resolving inefficiencies and bottlenecks.
- Integrate Generative AI within the ML Platform using state-of-the-art techniques.
Other
- MS and/or PhD in Computer Science, Machine Learning, or a related field, with at least 5 years of applied machine learning experience.
- Ability to work on tasks and projects through to completion with limited supervision.
- Passion for detail and follow through.
- Excellent communication skills and team player
- Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners.
