Job Description

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.