We are looking for a passionate fresher to work on
Computer Vision and
Retrieval-Augmented Generation (RAG) solutions. The 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. Entry-level computer vision roles commonly involve visual data processing, model support, integration work, and performance improvement, while RAG-focused roles increasingly emphasize embeddings, retrieval pipelines, and factual response grounding.upwork+2
You will work with mentors and engineering teams to develop, test, and improve AI models and applications. Beginner-oriented RAG learning paths and hiring guidance also emphasize hands-on experimentation, data preparation, evaluation, and practical integration skills rather than deep research specialization at the fresher level.machinelearningmastery+2
Key Responsibilities
- Assist in building computer vision models for image classification, object detection, OCR, and related tasks.
- Support the development of RAG pipelines using LLMs for intelligent search, question answering, and retrieval.
- Perform data collection, preprocessing, cleaning, annotation, and dataset organization.
- Help train, test, and evaluate ML models for accuracy and performance.
- Develop and integrate APIs and AI modules into applications.
- Work with embeddings, vector databases, and retrieval workflows where required.
- Document experiments, findings, and implementation details.
- Collaborate with team members to improve AI model quality and application performance. These responsibilities align well with published expectations for computer vision and RAG-oriented roles, especially around model building, data preparation, retrieval quality, integration, and continuous evaluation.upgrad+3
Requirements
- Basic knowledge of Python programming.
- Familiarity with ML or deep learning frameworks such as TensorFlow or PyTorch.
- Understanding of computer vision concepts such as image classification, object detection, or OCR.
- Basic understanding of NLP and large language model concepts.
- Exposure to embeddings, vector databases, or retrieval systems is preferred.
- Good problem-solving ability and willingness to learn new technologies quickly. Common learning and hiring guidance for these areas highlights Python, ML frameworks, visual-data fundamentals, and early exposure to LLM, retrieval, and evaluation concepts as the most relevant foundation for junior candidates.
Skills: ml,building,computer vision,concepts,models,integration,learning,data
