ML/AI Architect - Azure

Title: ML/AI Architect - Azure

Duration: 3 Months - Long Term

Location: Washington, DC 20433

Hybrid Onsite: 4 Days per week from Day1.

HM Comments: We are seeking an architect who is an expert in AI and Machine Learning areas.

Roles and Responsibilities:

The potential candidate 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.

Technical Requirements:

1. a) Strong proficiency in data modelling techniques and best practices, with a focus on designing models for AI applications.
2. b) Extensive experience in implementing and optimizing data pipelines using Azure cloud technologies, such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.
3. c) In-depth knowledge of Azure Machine Learning for model deployment, management, and operationalization.
4. d) Proficiency in programming languages commonly used in AI development, such as Python, R, and/or Scala.
5. e) Experience with AI-specific development frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn.
6. f) Familiarity with Azure Cognitive Services for integrating AI capabilities, such as natural language processing, computer vision, and speech recognition, into applications.
7. g) Strong understanding of SQL and NoSQL databases, particularly Azure SQL Database and Azure Cosmos DB, for efficient data storage and retrieval.
8. h) Experience in data cleansing, reformatting, and transforming tasks, including handling various file formats (CSV, JSON, Parquet, etc.), content types, and structures.
9. i) Proficiency in data profiling techniques and tools to identify data quality issues and anomalies.
10. j) Knowledge of data anonymization and data masking techniques to ensure data privacy and compliance with regulations.
11. k) Familiarity with version control systems, such as Git, for managing code and collaboration.
12. l) Experience in implementing and optimizing machine learning algorithms and models.
13. m) Strong problem-solving skills and the ability to troubleshoot and resolve technical issues related to data engineering and AI development.
14. n) Excellent understanding of cloud computing principles and distributed computing concepts.
15. o) Familiarity with DevOps practices and CI/CD pipelines for automated deployment and testing.
16. p) Strong knowledge of software engineering principles and best practices, including code documentation, testing, and maintainability.
17. q) Ability to work collaboratively in cross-functional teams and effectively communicate technical concepts to non-technical stakeholders.

“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”