Position Overview:

This role will play a critical role in implementing a proof of concept in the production environment and will needs to be a hands-on developer. The ideal candidate is passionate about understanding and experimenting with AI technologies that benefit society while minimizing risks.

Key Responsibilities:

• Collaborate with stakeholders to understand and refine customer-provided use cases for Generative AI solutions.
• Design, develop, and implement end-to-end Proofs of Concept (PoCs) using Azure AI and AWS Bedrock platforms.
• Build and maintain scalable, secure, and robust web applications, integrating Generative AI models and APIs.
• Develop both front-end and back-end components, ensuring seamless user experience and efficient data processing.
• Rapidly prototype and iterate on application features based on feedback and evolving requirements.
• Integrate cloud services and manage deployment pipelines for PoC applications.
• Document technical designs, development processes, and application architecture for knowledge sharing and future reference.
• Collaborate with cross-functional teams, including data scientists, UI/UX designers, and project managers, to deliver high-quality solutions.
• Conduct code reviews, testing, and debugging to ensure application reliability and performance.
• Stay current with emerging technologies and best practices in Generative AI and full stack development.

Qualifications:

Education:

• Master's and 5+ years of experience, Bachelor's and 7+ years of experience or 13+ years in lieu of a degree

Experience:

• 5+ years of experience in full stack development, including both front-end and back-end technologies.
• 3+ years of experience in Python development. You should be comfortable writing clean, efficient code.
• Hands-on experience developing applications using cloud platforms such as Microsoft Azure and/or Amazon Web Services.
• Familiarity with Generative AI concepts and experience integrating AI/ML models or APIs into applications.
• Proficiency in programming languages such as Python, JavaScript (Node.js, React, or Angular), or similar technologies.
• Experience with RESTful APIs, microservices architecture, and containerization (e.g., OpenShift or Docker).
• Strong understanding of software development best practices, version control (e.g., Bitbucket), and agile methodologies.
• Excellent problem-solving skills and ability to work collaboratively in a team environment.
• Clear and effective communication skills are necessary for collaborating with team members, presenting findings, and explaining complex AI concepts to non-technical stakeholders.

Skills:

• Proficiency in utilizing Microsoft Azure services, with a focus on AI and ML services such as Azure OpenAI, Azure AI Search, and Azure Vision.
• Understanding of fundamental AI and RAG concepts for developing generative AI applications.
• Commitment to ethical AI development, ensuring adherence to principles like fairness, transparency, accountability, and privacy in AI applications.
• Proficient in Python and familiar with current best practices and recent language features.
• Experience with Python web frameworks for building APIs and backend services.
• Strong experience in implementing and consuming RESTful web services.
• Solid experience with software development best practices, including unit testing, continuous integration with tools like Jenkins, and version control with Bitbucket.
• Experience with containerization and orchestration tools like Docker and OpenShift will be beneficial for deployment and scaling applications.
• Familiarity with Azure DevOps for automating builds, testing, and deployment processes within Azure.
• Understanding of compliance and security best practices within Azure, especially concerning handling sensitive data such as personal disability information.

Preferred Qualifications:

• Familiarity with the Azure OpenAI API and its capabilities for natural language processing (NLP) and generative modeling is highly desirable.
• Mastery of Azure AI services beyond the basics, including Azure Machine Learning, Azure Cognitive Services, Azure Databricks, and Azure Synapse Analytics, would make you a valuable asset. This includes understanding how to leverage these services in combination with Azure OpenAI frameworks for enhanced functionality and scalability.
• Ability to preprocess, clean, and manipulate data for RAG ingestion.
• Hands-on experience deploying generative AI models into production environments on Azure infrastructure is highly desirable. Understanding deployment considerations such as containerization, orchestration, monitoring, and security ensures smooth integration of AI solutions into real-world applications.
• Proficiency in C# and Java.
• Delivery (CI/CD) best practices and use of DevOps to accelerate quality releases to Production.
• Familiarity with data science tools, libraries, and frameworks (e.g., Jupyter Labs/Notebooks, pandas, PyTorch) is a strong plus.
• Awareness of issues and trends in Generative AI and Pythonic use of these is desirable.