AI Engineer - Open Source Models Specialist

Overview

Remote
Depends on Experience
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 12 Month(s)
No Travel Required
Able to Provide Sponsorship

Skills

AI
Collaborate
Computer Science
NLP
PyTorch
Python
Research
SPARQL
Strong communication skills
TensorFlow
communication skills
data scientists
information retrieval
machine learning
natural language processing
problem - solving
problem - solving skills
quality
software engineers
transformer
transformers

Job Details

AI Engineer - Open Source Models Specialist

Remote

Description :


1. Research, develop, and implement state-of-the-art AI models using open source frameworks and tools, with a focus on Hugging Face Datasets.
2. Fine-tune pre-trained models to specific tasks and domains, ensuring optimal performance and accuracy.
3. Build and optimize NLP encoders and transformers to support various natural language processing tasks and applications.
4. Design and maintain a scalable vector database for efficient storage and retrieval of AI embeddings and representations.
5. Develop semantic search algorithms and systems to enable intelligent information retrieval and recommendation.
6. Integrate knowledge graphs into AI models to enhance understanding and reasoning capabilities.
7. Collaborate with cross-functional teams including data scientists, software engineers, and product managers to deliver high-quality AI solutions.
8. Stay updated on the latest advancements in AI research and open source communities, and actively contribute to knowledge sharing within the team. Qualifications:
1. Bachelor's or Master's degree in Computer Science, Engineering, or related field.
2. Proven experience working with open source AI models, particularly in Hugging Face Datasets and fine-tuning techniques.
3. Strong programming skills in Python and proficiency with popular machine learning frameworks (e.g., TensorFlow, PyTorch).
4. In-depth understanding of NLP concepts, including word embeddings, attention mechanisms, and transformer architectures.
5. Familiarity with vector databases (e.g., Faiss, Milvus) and experience in designing and optimizing large-scale databases.
6. Expertise in semantic search methodologies and experience implementing search algorithms in real-world applications.
7. Knowledge of knowledge graph technologies (e.g., RDF, SPARQL) and experience in building and querying knowledge graphs.
8. Excellent problem-solving skills and ability to work independently as well as collaboratively in a fast-paced environment.
9. Strong communication skills and ability to effectively communicate technical concepts to both technical and non-technical stakeholders.