AI engineer

3 days on site in DC

Roles And Responsibilities For AI Engineer

The AI engineer will be responsible for designing, developing, and deploying AI models based on training data sets or using generative AI. The role will focus on leveraging Azure Cloud services to build enterprise-level solutions that meet the specific needs of the organization.

Key Responsibilities

Develop and implement AI models and algorithms.

Ability to build application including front end to show finished product.

Design and develop software applications that integrate AI technologies, including Generative AI, machine learning, and natural language processing.

Collaborate with data scientists and other stakeholders to identify business requirements and develop solutions that meet those needs.

Design and implement scalable and reliable software architectures that can handle large volumes of data and traffic.

Develop and maintain automated testing frameworks to ensure the quality and reliability of software applications.

Stay up-to-date with the latest AI and cloud-native technologies and trends, and apply them to improve software development processes and outcomes.

Work closely with cross-functional teams, including product managers, designers, and other engineers, to deliver high-quality software products.

Participate in code reviews, design reviews, and other team activities to ensure the quality and consistency of software development practices.

Design and implement cloud-based solutions using Azure services such as Azure Functions, Azure App Service, Azure Storage, and Azure Cosmos DB.

Implement and manage Azure DevOps pipelines for continuous integration and deployment of software applications.

Implement and maintain security and compliance controls for Azure resources, including network security groups, Azure Active Directory, and Azure Key Vault.

Collaborate with other teams, including operations and security, to ensure the availability, reliability, and security of Azure-based applications.

Selection Criteria

Minimum Education/Experience

• A Master’s degree with 5 years of relevant experience, or a bachelor’s degree with 7 years of relevant experience.
  
  

4. Technical Requirements:

• Strong proficiency in data modeling techniques and best practices, with a focus on designing models for AI applications.
• Extensive experience in implementing and optimizing data pipelines using Azure cloud technologies, such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.
• In-depth knowledge of Azure Machine Learning for model deployment, management, and operationalization.
• Proficiency in programming languages commonly used in AI development, such as Python, R, and/or Scala.
• Experience with AI-specific development frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn.
• Familiarity with Azure Cognitive Services for integrating AI capabilities, such as natural language processing, computer vision, and speech recognition, into applications.
• Strong understanding of SQL and NoSQL databases, particularly Azure SQL Database and Azure Cosmos DB, for efficient data storage and retrieval.
• Experience in data cleansing, reformatting, and transforming tasks, including handling various file formats (CSV, JSON, Parquet, etc.), content types, and structures.
• Proficiency in data profiling techniques and tools to identify data quality issues and anomalies.
• Knowledge of data anonymization and data masking techniques to ensure data privacy and compliance with regulations.
• Familiarity with version control systems, such as Git, for managing code and collaboration.
• Experience in implementing and optimizing machine learning algorithms and models.
• Strong problem-solving skills and the ability to troubleshoot and resolve technical issues related to data engineering and AI development.
• Excellent understanding of cloud computing principles and distributed computing concepts.
• Familiarity with DevOps practices and CI/CD pipelines for automated deployment and testing.
• Strong knowledge of software engineering principles and best practices, including code documentation, testing, and maintainability.
• Ability to work collaboratively in cross-functional teams and effectively communicate technical concepts to non-technical stakeholders.
• Teamwork and Collaboration: The candidate should be a team player and able to collaborate effectively with cross-functional teams, including designers, QA engineers, and project managers. They should be able to work in an agile development environment and actively participate in team discussions and meetings.
• Must be a clear and logical thinker with an open and innovative mind, and the ability to think outside the box.
• The ability to handle tough deadlines, and multiple demands from multiple sources.
• Communication and Documentation Skills: The candidate should possess excellent communication skills, both verbal and written. They should be able to effectively communicate with team members, stakeholders, and clients. Strong documentation skills are also important for creating technical documentation and user guides.
• Problem-Solving and Troubleshooting Abilities: The candidate should have a strong problem-solving mindset and be able to troubleshoot and debug issues in applications. They should be able to analyze complex problems, identify root causes, and propose effective solutions.
• Organization and Time Management: The candidate should be well-organized and able to manage multiple tasks and projects simultaneously. They should have the ability to prioritize tasks, meet deadlines, and deliver high-quality work. Ability to exercise technical vision while collaborating with other architects/engineers.
• Proven experience of collaborating with business partners and technical teams to develop technical requirements and design robust and flexible data solutions in alignment with the enterprise strategy.
• Continuous Learning and Adaptability: The candidate should have a passion for learning and staying updated with the latest industry trends and technologies. They should be adaptable to new tools, frameworks, and development methodologi