Arista Networks
Application Development Engineer - AI
Arista Networks, Nashua, New Hampshire, United States,
Job Description
Arista Networks seeks a Software Developer with expertise in building and implementing advanced software tools, machine learning models, and NLP/LLM solutions. The Application Development Engineer (ADE) will play a key role in developing ML-driven applications and leveraging LLMs to address complex, data-rich challenges, collaborating closely with cross-functional teams throughout the development lifecycle.
We value a proactive team player with attention to detail, strong problem-solving abilities, and experience with Agile frameworks. If you're experienced in deploying large language models, NLP applications, and advanced ML methodologies, we’d love to connect.
Who You’ll Work With
Work a team of Data Scientists in design, train and deploy models and partner with business users to train and fine tune before final deployment.
What You’ll Do
Collect, process, and clean large datasets from various structured and unstructured sources.
Develop NLP and LLM-based applications tailored to business needs, such as text analysis, summarization, and conversational agents.
Design, implement, and optimize machine learning and LLM models, including transformers and language generation techniques.
Apply statistical analysis, machine learning, and NLP techniques to solve complex, data-driven business problems.
Select appropriate data representation techniques for LLM models, including embeddings and tokenization.
Perform hyperparameter tuning, model evaluation, and retraining for LLMs and other ML systems as needed.
Develop and maintain data ingestion / parsing processes that support rapid prototyping and deployment of LLM-based solutions.
Visualize and present insights to stakeholders, leveraging data visualization tools.
Troubleshoot, debug, and enhance existing ML and AI systems.
Recommend and implement model improvements based on performance metrics.
Document technical processes, model architectures, and findings for reference and reporting.
Collaborate with cross-functional teams to develop and deploy data-driven, NLP-powered solutions.
Stay current with advancements in LLMs, and machine learning techniques.
Arista Networks seeks a Software Developer with expertise in building and implementing advanced software tools, machine learning models, and NLP/LLM solutions. The Application Development Engineer (ADE) will play a key role in developing ML-driven applications and leveraging LLMs to address complex, data-rich challenges, collaborating closely with cross-functional teams throughout the development lifecycle.
We value a proactive team player with attention to detail, strong problem-solving abilities, and experience with Agile frameworks. If you're experienced in deploying large language models, NLP applications, and advanced ML methodologies, we’d love to connect.
Who You’ll Work With
Work a team of Data Scientists in design, train and deploy models and partner with business users to train and fine tune before final deployment.
What You’ll Do
Collect, process, and clean large datasets from various structured and unstructured sources.
Develop NLP and LLM-based applications tailored to business needs, such as text analysis, summarization, and conversational agents.
Design, implement, and optimize machine learning and LLM models, including transformers and language generation techniques.
Apply statistical analysis, machine learning, and NLP techniques to solve complex, data-driven business problems.
Select appropriate data representation techniques for LLM models, including embeddings and tokenization.
Perform hyperparameter tuning, model evaluation, and retraining for LLMs and other ML systems as needed.
Develop and maintain data ingestion / parsing processes that support rapid prototyping and deployment of LLM-based solutions.
Visualize and present insights to stakeholders, leveraging data visualization tools.
Troubleshoot, debug, and enhance existing ML and AI systems.
Recommend and implement model improvements based on performance metrics.
Document technical processes, model architectures, and findings for reference and reporting.
Collaborate with cross-functional teams to develop and deploy data-driven, NLP-powered solutions.
Stay current with advancements in LLMs, and machine learning techniques.