Job
Description
We are looking for a highly skilled and experienced AI Engineer with 2 to 7 years of experience to join our team in Bengaluru. The ideal candidate will have a strong background in artificial intelligence, machine learning, and software engineering. ### Roles and Responsibility Design, develop, and deploy AI models and systems that align with business objectives. Conduct data analysis, pre-processing, and feature engineering to ensure high-quality input for AI algorithms. Train, evaluate, and optimize AI models using various machine learning algorithms and techniques. Implement scalable and efficient AI solutions in production environments, considering performance, latency, and reliability. Collaborate with AI and software engineers to integrate AI models into production systems and monitor their performance. Analyze and interpret AI model outputs, providing insights and recommendations to stakeholders. ### Job Requirements Deep understanding of AI and machine learning algorithms, including regression, classification, clustering, and deep learning. Proficiency in Python for AI model development, along with experience in deep learning frameworks such as TensorFlow, PyTorch, or Keras. Familiarity with cloud platforms, preferably Azure, to deploy scalable AI solutions, and knowledge of distributed computing frameworks like Spark. Strong foundation in software engineering practices, including version control with Git, testing, documentation, and code review. Understanding of NLP techniques and frameworks such as Hugging Face's Transformers, BERT, GPT, or Transformer models, TensorFlow's NLP library, or spaCy for language-related AI applications. Experience with natural language processing (NLP) techniques and frameworks, and generative AI techniques, including prompt engineering and fine-tuning LLMs. Proficiency in data pre-processing, visualization, SQL, and big data technologies, along with strong skills in Python for AI model development and implementation. Knowledge of containerization tools like Docker and an understanding of responsible AI practices and data privacy. Generative AI Techniques: Knowledge and experience in Generative AI techniques, prompt engineering, vector database, LLMs such as OpenAI, Azure OpenAI, Open-source LLMs. Fine-tuning: Collaborate with the research and development teams to fine-tune large language models using prompts, ensuring the models produce accurate, contextually relevant, and safe responses. Model Evaluation and Optimization: Experience with model evaluation, hyperparameter tuning, and optimization techniques. Data Pre-processing and Visualization: Solid understanding of techniques for data pre-processing, feature engineering, and data visualization. Cloud Platforms: Familiarity with cloud platforms like AWS, Azure, or GCP for scalable AI solutions. (Azure preferred) Distributed Computing: Knowledge of distributed computing frameworks, particularly Spark, for large-scale data processing. Version Control Systems: Proficient in using version control systems like Git for collaborative AI development. Troubleshooting and Debugging: Ability to troubleshoot and debug complex AI systems and models. Containerization: Knowledge of containerization tools like Docker for efficient deployment of AI solutions. Software Engineering Practices: Familiarity with software engineering practices, including testing, documentation, and code review.
