AI Engineer

Key Responsibilities:

• Design & Develop AI Models: Create and implement machine learning models and algorithms to solve business challenges.
• Data Processing & Analysis: Analyze large datasets, preprocess data, and apply statistical techniques to improve model accuracy.
• Deep Learning & NLP: Work with advanced AI techniques such as neural networks, reinforcement learning, and natural language processing to build intelligent systems.
• Model Optimization: Optimize models for performance, scalability, and efficiency using techniques such as hyperparameter tuning and model deployment.
• Collaboration: Collaborate with software engineers, data scientists, and business teams to integrate AI solutions into applications and platforms.
• Testing & Validation: Test and validate AI models, ensuring their effectiveness, and continuously improve them based on feedback and new data.
• Documentation: Write clear documentation for models, algorithms, and code to ensure maintainability and scalability.
• Research: Stay updated with the latest advancements in AI technologies and research to apply cutting-edge techniques.

Required Skills and Experience:

• Model Hosting and Deployment: Strong experience with deploying and managing machine learning models in production environments.
• Server Infrastructure: Proficient in building and managing servers to host and serve models for scalable access.
• APIs: Expertise in developing and deploying RESTful APIs for model inference.
• Redis & Celery: Solid experience with Redis for caching and Celery for task management and background job processing.
• PostgreSQL: Strong knowledge of PostgreSQL for data management and querying in the context of ML applications.
• Model Repositories: Experience with version control and repositories for machine learning models, including integration with Git or similar platforms.
• Cloud Platforms: Experience with cloud services (e.g., AWS, GCP, Azure) for deploying and managing models at scale.

Preferred Skills:

• Familiar with CI/CD pipelines for AI models to ensure automated testing, deployment, and updates.
• Experience with containerization technologies such as Docker and Kubernetes for deployment and orchestration.
• Familiarity with frameworks like TensorFlow Serving, TorchServe, or similar for serving models in production.
• Knowledge of model monitoring tools and logging frameworks.
• Ability to work in a fast-paced, collaborative environment with cross-functional teams.
• Knowledge in consumer and server grade GPU to handle CUDA compatability issues.