Junior - AI/ML

Job Description

Roles and Responsibilities:

• Collaborate with the AI/ML team to understand project requirements and convert them into implementable AI/ML solutions
• Develop and implement AI/ML models for various use cases such as Generative AI, RAG, Chatbots, Object Detection, Semantic Searching, Entity Recognition, etc.
• Perform data preprocessing, feature engineering, and model evaluation tasks
• Assist in data annotation and creation of high-quality training datasets
• Conduct experiments and analyze results to optimize model performance
• Ensure adherence to coding standards, best practices, and guidelines in the development process
• Participate in code reviews and refactor code based on provided feedback
• Validate developed models against requirements and test cases
• Understand and adhere to application security aspects in AI/ML development
• Assist in model deployment and serving using appropriate tools and techniques
• Maintain comprehensive documentation, code comments, and knowledge retention for AI/ML projects
• Actively participate in scrum ceremonies and maintain tracking using relevant project management tools
• Uphold the organization's values, vision, and mission while fostering a collaborative work environment
• Actively participate in training sessions provided by the organization to enhance AI/ML skills

Skills (Must have):

• Fundamental understanding of AI/ML concepts, algorithms, and techniques
• Proficiency in programming languages commonly used in AI/ML, such as Python
• Hands-on experience with popular AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn)
• Familiarity with data preprocessing, feature engineering, and model evaluation techniques
• Knowledge of version control systems (Git) and experience with project management tools (Jira, Asana, etc.)
• Good communication skills and ability to work collaboratively in a team environment
• Bachelor's degree in Computer Science, Data Science, or a related field 1-2 years of experience in AI/ML development

Skills (Good to have):

• Familiarity with Natural Language Processing (NLP) techniques and libraries (e.g., NLTK, spaCy, Transformers)
• Basic understanding of Generative AI techniques and architectures (e.g., Transformer-based models, GANs, VAEs)
• Experience with Retrieval-Augmented Generation (RAG) and its implementation using LLMs and Vector Databases
• Understanding of Vector Databases and their applications in AI/ML (e.g., Faiss, Chroma, Azure Search, Elastic search)
• Knowledge of Semantic Search and its implementation using embeddings and similarity measures
• Knowledge of computer vision and image processing techniques (OpenCV, Pillow, scikit-image)
• Familiarity with data annotation tools and techniques for creating high-quality training datasets
• Knowledge of model deployment and serving techniques (e.g., Flask, FastAPI, Cloud deployments)
• Understanding of machine learning pipelines and MLOps practices Familiarity with cloud platforms (AWS, Azure, GCP) and their AI/ML offerings