GenAI Engineer

Fusion

Phoenix, AZ, United States

about 1 month ago

Apply on www.indeed.com

Prepare application

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Compensation

$91,082.0 - $109,690.0

Working hours

Regular working hours

Job source

www.indeed.com

Tech stack

C (Programming Language) Java (Programming Language) Artificial Intelligence Amazon Web Services Bash Shell Big Data Databases Extract Transform Load (ETL) Data Mining Database Design Apache Hadoop Python (Programming Language)

+20 more

Unix Shell Machine Learning Natural Language Processing Cloud Services Tensorflow Azure Machine Learning SAS (Software) SQL Databases Systems Integration Pytorch Large Language Models Apache Spark Generative AI Fastapi Spark Mllib Statistics Packages Vba Programming Language Data Analytics Data Management Data Pipelines

Job description

Design, develop, and optimize generative AI models utilizing frameworks such as TensorFlow, PyTorch, and other machine learning tools.
Strong exp in Python, FastAPI, RAG, LLM’s
Implement scalable data pipelines using ETL processes, Hadoop, Spark, and SQL databases to support model training and deployment.
Conduct statistical analysis and modeling to evaluate AI models’ performance, accuracy, and robustness for predictive analytics and natural language processing tasks.
Collaborate with cross-functional teams to integrate AI solutions into existing systems using Java, Python, C, or VBA for seamless deployment.
Leverage cloud-based machine learning services on AWS or similar platforms to enhance model scalability and efficiency.
Perform data mining and data analytics on large datasets to extract insights that inform model improvements and business strategies.
Develop comprehensive documentation for model training procedures, statistical analysis tools, and deployment processes to ensure transparency and reproducibility.

Requirements

Proven experience in developing AI models with a focus on generative AI and natural language processing applications.
Strong background in machine learning frameworks such as TensorFlow, Spark MLlib, or similar tools.
Hands-on experience with big data systems including Hadoop, Spark implementation, and SQL databases for large-scale data processing.
Familiarity with statistical analysis tools like R or SAS for research-oriented statistical modeling.
Proficiency in programming languages including Python, Java, C, Bash (Unix shell), and VBA for automation and system integration tasks.
Experience deploying machine learning models using cloud services like AWS Machine Learning or similar platforms.
Knowledge of database design principles and experience working with SQL databases for efficient data management.
Understanding of quantum engineering concepts is a plus but not mandatory; a strong foundation in scalable AI implementation is essential.