AI Developer

Key Responsibilities
  • Design, develop, and deploy machine learning models, with a focus on large language models (LLMs), ensuring scalability and performance.
  • Handle large datasets, perform preprocessing, feature engineering, and prepare data pipelines for training.
  • Fine-tune pre-trained LLMs (e.g., GPT, BERT, etc.) for specific use cases, ensuring optimal performance.
  • Stay updated with the latest trends in AI/ML, particularly in LLMs and deep learning, to propose innovative solutions.
  • Continuously test, monitor, and optimize models for accuracy, speed, and efficiency.
  • Maintain clear documentation for model development, testing procedures, and performance metrics.
  • Integrate AI models and LLM such as those from Microsoft Azure, OpenAI, Google Cloud, and AWS into scalable and robust applications.
  • Build and maintain RESTful APIs and microservices to support AI functionalities.
  • Implement front-end components using modern frameworks (e.g., React, Angular, Vue.js) and ensure responsive design and user experience.
  • Write clean, maintainable, and efficient code while adhering to best practices in software development.
  • Troubleshoot and resolve issues, ensuring high availability and performance of AI-enabled applications.
Desired Experience & Skills
  • Bachelor's degree in Computer Science, Engineering, or a related field.
  • Proven experience with a strong portfolio of AI-enabled products (1+ year).
  • Proficiency in front-end technologies such as HTML, CSS, JavaScript, and modern frameworks (React, Angular, Vue.js).
  • Expertise in back-end development using languages such as Python and Node.js.
  • Hands-on experience with cloud services and AI APIs such as those from Microsoft Azure, OpenAI, Google Cloud, and AWS.
  • Knowledge of database technologies (SQL, NoSQL) and data modeling.
  • Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
  • Excellent communication skills and the ability to articulate technical concepts to non-technical stakeholders.
  • Uses AI tools and automation to boost code generation, debugging, and deployment.
  • Strong knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, Hugging Face, etc.).
  • Proficiency in Python, with experience using libraries like NumPy, pandas, scikit-learn, etc.
  • Experience with model fine-tuning and transfer learning in LLMs.
  • Familiarity with cloud platforms (AWS, Google Cloud, or Azure) for model deployment.
  • Knowledge of NLP concepts such as tokenization, attention mechanisms, embeddings, etc.
  • Strong analytical and problem-solving skills, with the ability to troubleshoot and optimize machine learning models.
  • Excellent communication skills to collaborate with teams and present complex ideas clearly.

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