*100% Remote role* Our large retail client is seeking a talented and experienced Azure AI Engineer to join their dynamic team. The ideal candidate will be responsible for designing, developing, and deploying AI solutions using Azure AI services. You will collaborate with the business and IT engineers to integrate AI capabilities into applications.
Due to client requirement, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.
Rate: $70 - $85 / hr. w2
Responsibilities:
Model Training and Evaluation: Train, fine-tune, and evaluate AI models, including LLMs, to ensure they meet performance criteria and integrate into current solutions
API Development and Integration: Develop and integrate APIs to enable seamless interaction between AI models and other applications for serving models
Collaboration: Proven ability to work collaboratively in cross-functional teams, including data scientists, software engineers, and business stakeholders.
Performance Optimization: Techniques for optimizing AI models for performance, including hyperparameter tuning and model compression
Support: Monitor and maintain AI systems. Provide technical support and troubleshooting for AI solutions.
Documentation: Create comprehensive documentation for AI solutions, including design documents, user guides, and operational procedures.
Continuous Learning: Demonstrate commitment to staying updated with the latest advancements in AI, machine learning, and cloud technologies.
Experience Requirements:
Cloud Services: Deep understanding of Azure AI services, including Azure Machine Learning, Azure Cognitive Services, and Azure OpenAI Service.
Large Language Models (LLMs): Experience with LLMs such as GPT-3, GPT-4, or similar models, including fine-tuning and deploying them.
API Development: Proficiency in developing and consuming RESTful APIs, and familiarity with GraphQL.
Data Engineering: Skills in data preprocessing, feature engineering, and data pipeline development using tools like Azure Data Factory.
Containerization and Orchestration: Experience with Docker and Kubernetes for containerizing AI solutions and managing their deployment.
Bachelor's degree in computer science, engineering, or related field.
AI/ML Development: At least 3+ years of hands-on experience in developing AI/ML solutions, with a focus on deploying them in a cloud environment.
Experience with Azure AI services, cloud computing and version control systems like Git.
Strong programming skills in Python and other relevant languages.
Experience with data engineering and MLOps practices including CI/CD for machine learning models.
Excellent problem-solving and communication skills.
Knowledge of security best practices for deploying AI solutions, including data encryption and access control
Understand ethical considerations in AI, including bias detection and mitigation strategies