JobRialto
ML Engineer
JobRialto, Charlotte, North Carolina, United States, 28245
Job Summary
We are seeking a skilled ML Engineer to design and optimize scalable machine learning pipelines and distributed training systems within the ITOT Products space. The ideal candidate will have expertise in building and deploying state-of-the-art models, including fine-tuning large language models (LLMs), and ensuring seamless model performance, scalability, and system optimization.
Key Responsibilities
•Build scalable machine learning pipelines for model training and deployments.
•Leverage distributed training systems for optimized execution of model hyperparameter tuning, training, and inference.
•Research and implement cutting-edge LLM models, including fine-tuning and serving for diverse business applications.
•Optimize integration between machine learning libraries and cloud ML/data processing frameworks.
•Design solutions to improve performance on CPUs/GPUs and address system-level bottlenecks.
•Ensure uptime and scalability of ML models, maintaining high code quality and thoughtful design.
•Develop deep learning models with optimal parallelism and performance.
•Communicate complex technical concepts effectively to non-technical audiences.
Required Qualifications
•MS or PhD in Computer Science, Software Engineering, Electrical Engineering, or related fields.
•3+ years of experience with Python in a programming-intensive role.
•3+ years of experience with distributed computing frameworks (e.g., Spark, Kubernetes).
•3+ years of experience with popular ML frameworks such as TensorFlow, PyTorch, Keras, HuggingFace Transformers, etc.
•3+ years of experience with major cloud services (e.g., Azure, AWS, Google Cloud).
•2+ years of experience with machine learning topics such as classification, clustering, optimization, recommendation systems, or deep learning.
•Experience building and scaling Generative AI applications (e.g., Langchain, PGVector, Pinecone, Azure ML).
•Proven track record in building data products with a focus on innovation.
Preferred Qualifications
•Proficiency in containerization services and CI/CD frameworks.
•Expertise in Azure ML for model deployment.
•Advanced Python and PySpark coding skills.
•Experience in scalable service architecture using FastAPI.
Education:
Doctoral Degree, Masters Degree
We are seeking a skilled ML Engineer to design and optimize scalable machine learning pipelines and distributed training systems within the ITOT Products space. The ideal candidate will have expertise in building and deploying state-of-the-art models, including fine-tuning large language models (LLMs), and ensuring seamless model performance, scalability, and system optimization.
Key Responsibilities
•Build scalable machine learning pipelines for model training and deployments.
•Leverage distributed training systems for optimized execution of model hyperparameter tuning, training, and inference.
•Research and implement cutting-edge LLM models, including fine-tuning and serving for diverse business applications.
•Optimize integration between machine learning libraries and cloud ML/data processing frameworks.
•Design solutions to improve performance on CPUs/GPUs and address system-level bottlenecks.
•Ensure uptime and scalability of ML models, maintaining high code quality and thoughtful design.
•Develop deep learning models with optimal parallelism and performance.
•Communicate complex technical concepts effectively to non-technical audiences.
Required Qualifications
•MS or PhD in Computer Science, Software Engineering, Electrical Engineering, or related fields.
•3+ years of experience with Python in a programming-intensive role.
•3+ years of experience with distributed computing frameworks (e.g., Spark, Kubernetes).
•3+ years of experience with popular ML frameworks such as TensorFlow, PyTorch, Keras, HuggingFace Transformers, etc.
•3+ years of experience with major cloud services (e.g., Azure, AWS, Google Cloud).
•2+ years of experience with machine learning topics such as classification, clustering, optimization, recommendation systems, or deep learning.
•Experience building and scaling Generative AI applications (e.g., Langchain, PGVector, Pinecone, Azure ML).
•Proven track record in building data products with a focus on innovation.
Preferred Qualifications
•Proficiency in containerization services and CI/CD frameworks.
•Expertise in Azure ML for model deployment.
•Advanced Python and PySpark coding skills.
•Experience in scalable service architecture using FastAPI.
Education:
Doctoral Degree, Masters Degree