Arbisoft is seeking a skilled Machine Learning Engineer to join their team in developing and deploying advanced AI solutions. This role focuses on working with large language models (LLMs), retrieval-augmented generation (RAG) pipelines, and agentic workflows to create innovative and scalable machine learning systems. The ideal candidate will have strong expertise in Python programming, experience with transformer architectures, and the ability to build and maintain cloud-based ML infrastructure.
Key Responsibilities:
- Design, develop, and deploy machine learning and deep learning models using frameworks such as PyTorch and TensorFlow.
- Build and optimize large language models and related AI pipelines, including RAG systems and agentic workflows.
- Collaborate with cross-functional teams to integrate ML solutions into production environments with a focus on scalability and reliability.
- Conduct rigorous evaluation and fine-tuning of models to improve performance and accuracy.
- Develop and maintain cloud-based infrastructure for training, deploying, and monitoring ML models.
- Stay current with the latest research and advancements in AI and machine learning to incorporate best practices and innovative techniques.
- Document processes, models, and workflows to ensure reproducibility and knowledge sharing within the team.
Required Qualifications:
- Proven experience in machine learning engineering, particularly with Python programming.
- Strong proficiency with deep learning frameworks such as PyTorch and TensorFlow.
- Hands-on experience working with transformer models and natural language processing (NLP) techniques.
- Familiarity with building and deploying scalable ML systems on cloud platforms.
- Solid understanding of ML model evaluation metrics and techniques for model optimization.
- Ability to work effectively in a collaborative, fast-paced environment and communicate technical concepts clearly.
Preferred Qualifications and Benefits:
- Experience with retrieval-augmented generation (RAG) pipelines and agentic AI workflows is highly desirable.
- Knowledge of containerization and orchestration tools such as Docker and Kubernetes.
- Background in research or contributions to open-source ML projects.
- Exposure to MLOps practices including CI/CD pipelines for ML models.
- Opportunity to work on cutting-edge AI technologies and contribute to impactful projects.
- Collaborative work culture that encourages continuous learning and professional growth.
This position offers the chance to be at the forefront of AI innovation, working alongside a talented team to build next-generation machine learning solutions that drive real-world impact.
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