Architect – RAG GenAI Applications(Web/Mobile with Python Middle Tier)

We are seeking an experienced AI Architect with expertise in Python to design, develop, and deploy Retrieval-Augmented Generation (RAG) applications for web and mobile platforms. This role will focus on architecting the middle tier of the application, where Python will be used to manage AI model integration, data processing, and backend logic that connects with frontend (web or mobile) applications. You will ensure that AI-powered features are fast, scalable, and optimized for both user experience and performance.
 

- Backend and Middle Tier Architecture: Lead the design and development of the middle tier of the application using Python to integrate RAG AI models and provide APIs for web/mobile frontend consumption. Manage the interaction between retrieval-augmented techniques and generative models.
- AI Model Integration with Python: Build Python services that connect AI models (e.g., transformers, embeddings, and vector search) to handle tasks such as query retrieval, model inference, and generating responses. Leverage frameworks like Flask, FastAPI, or Django for API development.
- Data Pipeline and Model Deployment: Design Python-based data pipelines to preprocess, clean, and prepare data for AI model training, as well as for serving the models in production environments. Optimize these pipelines to support both batch and real-time data processing.
- Optimization for Mobile/Web Performance: Ensure Python middle-tier services are optimized for mobile and web platforms, managing AI model inference and retrieval processes to minimize latency and maximize throughput. Focus on efficient API calls and reducing network overhead.
- Cross-Platform Integration: Work closely with frontend engineers to ensure smooth communication between Python-based backend services and the web/mobile UI. Implement RESTful APIs or GraphQL endpoints for seamless frontend-backend interaction.
- Cloud and Infrastructure Management: Implement cloud solutions to host Python-based services, ensuring that AI models are scalable and that the infrastructure can handle high traffic. Leverage containerization (Docker) and orchestration (Kubernetes) for model deployment and management.
- Security and Data Privacy: Implement secure data handling mechanisms in Python, ensuring data privacy and compliance with standards such as GDPR and CCPA. Ensure the backend services are secure and robust against threats.
- Monitoring and Logging: Set up monitoring, logging, and alerting for Python backend services, ensuring smooth operation of AI features. Use tools like Prometheus, Grafana, and ELK stack for real-time performance tracking.
- AI Model Performance and Optimization: Continuously optimize model performance by fine-tuning and adapting Python-based AI models for real-time use cases. Manage trade-offs between computation load, response time, and quality of generated content.
- Collaboration: Partner with data scientists, machine learning engineers, and mobile/web developers to ensure tight integration between AI models, mobile/web front-end, and backend infrastructure.

- Experience:
  - 8+ Strong experience in working with web/mobile technologies (e.g., React, React Native, Flutter, Django, Flask, FastAPI).
  - 3+ years of experience in building AI applications with a focus on NLP, machine learning, generative models, and retrieval-augmented systems.
  - 5+ years of experience working with Python in building scalable backend services and middle-tier architectures for web or mobile platforms.
  - Proven experience in designing and deploying AI systems that integrate retrieval-based techniques (e.g., FAISS, Weaviate) and generative models (e.g., GPT, BERT).
  - Expertise in cloud platforms (e.g., AWS, GCP, Azure) and deployment of Python-based microservices.
  - Building RESTful APIs or GraphQL services (using frameworks like Flask, FastAPI, or Django).
  - Handling AI model inference and data processing (using libraries like NumPy, Pandas, TensorFlow, PyTorch, and Hugging Face Transformers).
  - Integrating vector search solutions (e.g., FAISS, Pinecone, Weaviate) with the AI models for efficient retrieval-augmented generation.
  - Optimizing performance for mobile/web, ensuring minimal latency and high responsiveness, while offloading heavy computations where necessary. 
  - Experience with containerization (Docker) and Kubernetes for deploying scalable Python-based services.
  - Proficient in cloud infrastructure management, with a focus on managing Python services in the cloud.

- Skills:
  - Advanced proficiency in Python for building backend services and data processing pipelines. Familiarity with frameworks like Flask, Django, and FastAPI.
  - Experience with AI libraries and frameworks (TensorFlow, PyTorch, Hugging Face Transformers).
  - Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate) and integration with retrieval-augmented systems.
  - Strong knowledge of RESTful API design, GraphQL, and API security best practices (e.g., OAuth, JWT).

- Soft Skills:
  - Excellent problem-solving abilities and a strong focus on creating highly scalable and performant solutions.
  - Strong communication skills, with the ability to collaborate across teams (data science, frontend, product).
  - Ability to mentor junior team members and lead technical discussions.