Artificial Intelligence Engineer Job at Xcede in Cary
Xcede, Cary, NC, United States
Responsibilities
Design, develop, and deploy machine learning and AI models for business applications
Build and optimize data pipelines for model training, validation, and inference
Integrate AI models into applications using REST APIs, microservices, or cloud services
Perform model evaluation, tuning, and performance optimization
Monitor models in production for accuracy, drift, and reliability
Collaborate with product managers, data scientists, and engineers to translate requirements into AI solutions
Implement MLOps practices including versioning, CI/CD, and automated deployments
Ensure adherence to Responsible AI, security, data privacy, and compliance standards
Document models, pipelines, and deployment procedures
Required Qualifications
Strong programming skills in Python (mandatory)
Solid understanding of machine learning algorithms and statistics
Hands on experience with ML/DL frameworks (TensorFlow, PyTorch, Scikit-Learn)
Experience working with structured and unstructured data
Knowledge of SQL and data processing libraries (Pandas, NumPy)
Experience deploying models using APIs, Docker, and cloud platforms (AWS/Azure/GCP)
Familiarity with Git, CI/CD pipelines, and software engineering best practices
Preferred Skills
Experience with Generative AI / LLMs (OpenAI, Azure OpenAI, Hugging Face)
Knowledge of MLOps tools (MLflow, Kubeflow, Airflow)
Experience with big data technologies (Spark, Databricks)
Exposure to computer vision or NLP use cases
Experience designing, developing, and deploying machine learning and AI models for business applications
Building and optimizing data pipelines for model training, validation, and inference
Integrating AI models into applications using REST APIs, microservices, or cloud services
Performing model evaluation, tuning, and performance optimization
Monitoring models in production for accuracy, drift, and reliability
Collaborating with product managers, data scientists, and engineers to translate requirements into AI solutions
Implementing MLOps practices including versioning, CI/CD, and automated deployments
Ensuring adherence to Responsible AI, security, data privacy, and compliance standards
Documenting models, pipelines, and deployment procedures
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Design, develop, and deploy machine learning and AI models for business applications
Build and optimize data pipelines for model training, validation, and inference
Integrate AI models into applications using REST APIs, microservices, or cloud services
Perform model evaluation, tuning, and performance optimization
Monitor models in production for accuracy, drift, and reliability
Collaborate with product managers, data scientists, and engineers to translate requirements into AI solutions
Implement MLOps practices including versioning, CI/CD, and automated deployments
Ensure adherence to Responsible AI, security, data privacy, and compliance standards
Document models, pipelines, and deployment procedures
Required Qualifications
Strong programming skills in Python (mandatory)
Solid understanding of machine learning algorithms and statistics
Hands on experience with ML/DL frameworks (TensorFlow, PyTorch, Scikit-Learn)
Experience working with structured and unstructured data
Knowledge of SQL and data processing libraries (Pandas, NumPy)
Experience deploying models using APIs, Docker, and cloud platforms (AWS/Azure/GCP)
Familiarity with Git, CI/CD pipelines, and software engineering best practices
Preferred Skills
Experience with Generative AI / LLMs (OpenAI, Azure OpenAI, Hugging Face)
Knowledge of MLOps tools (MLflow, Kubeflow, Airflow)
Experience with big data technologies (Spark, Databricks)
Exposure to computer vision or NLP use cases
Experience designing, developing, and deploying machine learning and AI models for business applications
Building and optimizing data pipelines for model training, validation, and inference
Integrating AI models into applications using REST APIs, microservices, or cloud services
Performing model evaluation, tuning, and performance optimization
Monitoring models in production for accuracy, drift, and reliability
Collaborating with product managers, data scientists, and engineers to translate requirements into AI solutions
Implementing MLOps practices including versioning, CI/CD, and automated deployments
Ensuring adherence to Responsible AI, security, data privacy, and compliance standards
Documenting models, pipelines, and deployment procedures
#J-18808-Ljbffr