AI DEVELOPER

Responsibilities : 

Design, develop, and implement AI-powered features and applications using Python and Java.

Integrate with OpenAI and Azure AI services, including LLMs, for natural language processing, text generation, and other AI functionalities.

Work with vector databases to efficiently store, retrieve, and manage embeddings for semantic search and other AI-related tasks.

Develop and maintain APIs and microservices to connect AI models with other systems.

Collaborate with data scientists and other engineers to build and deploy machine learning models.

Write clean, efficient, and well-documented code.

Participate in code reviews and contribute to improving our development processes.

Troubleshoot and debug issues related to AI integrations and application performance.

Stay up-to-date with the latest advancements in AI, LLMs, and related technologies.

Contribute to the overall architecture and design of our AI platform.

Technical Skills

Basic Data Architecture & Modeling
• Understanding how to structure and organize data for scalable microservices and AI-driven applications.
• Experience designing solutions using vector databases (e.g., Pinecone, Weaviate, Chroma).

Database Management
• Familiarity with SQL and NoSQL databases.
• Experience working with cloud-based databases and vector stores for AI and analytics use cases.

Data Collection Tools & Platforms
• Experience integrating data through RESTful APIs and custom connectors.
• Hands-on experience with OpenAI GPT models and Azure AI services using Retrieval-Augmented Generation (RAG) architecture.

Basic Analytics
• Exposure to analytics tools and techniques for monitoring AI model performance and service metrics.

ETL Processes
• Knowledge of building and maintaining Extract, Transform, Load (ETL) pipelines to support AI and microservices workflows. 

Analytical Skills

Data Quality Assurance
• Ability to define validation rules and ensure data accuracy and completeness in AI model inputs and outputs.
• Experience with version control systems (e.g., Git) to maintain code and data integrity.

KPI & Metric Definition
• Collaborate with stakeholders to define performance metrics for AI models and service-level indicators. 

Strategic & Project Management Skills

Requirements Gathering
• Work with cross-functional teams to understand project goals, data needs, and AI integration requirements.

Documentation & Specification Writing
• Create clear technical documentation for API integrations, data pipelines, and AI service configurations.

Stakeholder Communication
• Translate complex AI and data engineering concepts into business-friendly language for diverse stakeholders.

Agile Methodologies
• Familiarity with agile project management practices for iterative development and deployment of AI-powered solutions.