AI Engineer Fresher

Job Description: We are seeking a passionate fresher to join our team and work on Computer Vision and Retrieval-Augmented Generation (RAG) solutions. Your role will involve building AI-driven applications, working with image and video data, and supporting intelligent search and retrieval systems powered by LLMs, embeddings, and modern ML tooling. Key Responsibilities: - Assist in building computer vision models for tasks such as image classification, object detection, OCR, and related functions. - Support the development of RAG pipelines using LLMs to enable intelligent search, question answering, and retrieval. - Perform tasks including data collection, preprocessing, cleaning, annotation, and dataset organization. - Help in training, testing, and evaluating ML models to ensure accuracy and performance. - Develop and integrate APIs and AI modules into applications. - Work with embeddings, vector databases, and retrieval workflows as necessary. - Document experiments, findings, and implementation details. - Collaborate with team members to enhance AI model quality and application performance. These responsibilities are in line with industry expectations for computer vision and RAG-oriented roles, focusing on model building, data preparation, retrieval quality, integration, and continuous evaluation. Qualifications Required: - Basic knowledge of Python programming. - Familiarity with ML or deep learning frameworks like TensorFlow or PyTorch. - Understanding of computer vision concepts such as image classification, object detection, or OCR. - Basic knowledge of NLP and large language model concepts. - Exposure to embeddings, vector databases, or retrieval systems is preferred. - Good problem-solving skills and a willingness to quickly learn new technologies are essential. Junior candidates are encouraged to have a foundation in Python, ML frameworks, visual-data fundamentals, and early exposure to LLM, retrieval, and evaluation concepts. Additional Details: The role will provide you with opportunities to work with mentors and engineering teams, allowing you to develop, test, and enhance AI models and applications. Additionally, beginner-oriented RAG learning paths and hiring guidance emphasize practical skills such as hands-on experimentation, data preparation, evaluation, and integration over deep research specialization at the fresher level. Collaboration with team members is crucial for improving AI model quality and application performance. Job Description: We are seeking a passionate fresher to join our team and work on Computer Vision and Retrieval-Augmented Generation (RAG) solutions. Your role will involve building AI-driven applications, working with image and video data, and supporting intelligent search and retrieval systems powered by LLMs, embeddings, and modern ML tooling. Key Responsibilities: - Assist in building computer vision models for tasks such as image classification, object detection, OCR, and related functions. - Support the development of RAG pipelines using LLMs to enable intelligent search, question answering, and retrieval. - Perform tasks including data collection, preprocessing, cleaning, annotation, and dataset organization. - Help in training, testing, and evaluating ML models to ensure accuracy and performance. - Develop and integrate APIs and AI modules into applications. - Work with embeddings, vector databases, and retrieval workflows as necessary. - Document experiments, findings, and implementation details. - Collaborate with team members to enhance AI model quality and application performance. These responsibilities are in line with industry expectations for computer vision and RAG-oriented roles, focusing on model building, data preparation, retrieval quality, integration, and continuous evaluation. Qualifications Required: - Basic knowledge of Python programming. - Familiarity with ML or deep learning frameworks like TensorFlow or PyTorch. - Understanding of computer vision concepts such as image classification, object detection, or OCR. - Basic knowledge of NLP and large language model concepts. - Exposure to embeddings, vector databases, or retrieval systems is preferred. - Good problem-solving skills and a willingness to quickly learn new technologies are essential. Junior candidates are encouraged to have a foundation in Python, ML frameworks, visual-data fundamentals, and early exposure to LLM, retrieval, and evaluation concepts. Additional Details: The role will provide you with opportunities to work with mentors and engineering teams, allowing you to develop, test, and enhance AI models and applications. Additionally, beginner-oriented RAG learning paths and hiring guidance emphasize practical skills such as hands-on experimentation, data preparation, evaluation, and integration over deep research specialization at the fresher level. Collaboration with team members is crucial for improving AI model quality and application performance.