Job
Description
Basic qualification: Bachelors degree in Computer Science, Data Science, Artificial Intelligence, or a related field. 0-1 year of experience in ML/AI (internship, academic project, or freelance experience acceptable). Exposure to cloud services (AWS, Azure, or GCP). Familiarity with ML experiment tracking (e.g., MLflow, Weights & Biases). Basic knowledge of model deployment frameworks (Flask, FastAPI, Streamlit). Experience with Git/GitHub and Agile tools (e.g., Jira, Trello). Technical Skills: Proficiency in Python and key libraries (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch). Basic understanding of machine learning concepts (e.g., classification, regression, neural networks). Exposure to generative AI models such as GPT, GANs, VAEs, or diffusion models. Familiarity with tools such as Hugging Face Transformers, OpenAI APIs, or LangChain. Experience with data formats like CSV, JSON, and basic SQL knowledge. Understanding of REST APIs and basic model integration. Soft Skills: Strong communication and collaboration abilities. Eagerness to learn and work in a fast-paced environment. Strong problem-solving and analytical thinking. Attention to detail and documentation practices. Roles and Responsibilities: Assist in designing, developing, and deploying machine learning models for structured and unstructured data. Fine-tune and evaluate generative AI models such as GPT, Gemini, LLAMA, etc. Perform data collection, cleaning, transformation, and feature engineering. Contribute to prompt engineering, embeddings, and optimization of generative pipelines. Support the integration of AI models into production systems or APIs. Write clean, testable, and efficient Python code using ML/AI libraries. Track and evaluate model performance using appropriate metrics and tools. Collaborate closely with cross-functional teams (engineering, data, product). Document workflows, model experiments, and pipeline processes. Stay current with the latest trends and research in machine learning and generative AI.
