GenAI Developer

Introduction:

This role plays a critical part in implementing proof-of-concept solutions in a production environment. The ideal candidate is a hands-on developer who is passionate about understanding and experimenting with AI technologies that benefit society while minimizing risks.

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.

Requirements:

Education & Experience Requirements:

• Master''''''''s degree and 5+ years of experience; OR
• Bachelor''''''''s degree and 7+ years of experience; OR
• 13+ years of relevant experience in lieu of a degree

Basic Qualifications:

• 5+ years of experience in full stack development, including both front-end and back-end technologies.
• 3+ years of Python development experience; must 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 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.
• Clear and effective communication skills for collaborating with team members, presenting findings, and explaining complex AI concepts to non-technical stakeholders.

Required Skills:

• Proficiency in 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 of fairness, transparency, accountability, and privacy.
• Experience with Python web frameworks for building APIs and backend services.
• Strong experience implementing and consuming RESTful web services.
• Solid experience with software development best practices including unit testing, continuous integration (Jenkins), and version control (Bitbucket).
• Experience with containerization and orchestration tools such as Docker and OpenShift.
• Familiarity with Azure DevOps for automating builds, testing, and deployment processes.
• Understanding of compliance and security best practices within Azure, especially for handling sensitive data.

Desired Skills (Nice to Have):

• Familiarity with the Azure OpenAI API and its NLP and generative modeling capabilities.
• Advanced knowledge of Azure AI services including Azure Machine Learning, Cognitive Services, Databricks, and Synapse Analytics.
• Ability to preprocess, clean, and manipulate data for RAG ingestion.
• Hands-on experience deploying generative AI models to production environments on Azure infrastructure.
• Proficiency in C# and Java.
• CI/CD best practices and DevOps experience to accelerate quality releases to production.
• Familiarity with data science tools and frameworks such as Jupyter Labs/Notebooks, pandas, and PyTorch.
• Awareness of current issues and trends in Generative AI.