Junior ML Engineer (Onsite, Lahore, Remittance Salary)

Requirements:

Bachelors degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or a related field.
1–2 years of hands-on experience in machine learning or AI development.
Experience working on at least 1–2 end-to-end machine learning projects in academic or professional environments.
Exposure to NLP, computer vision, or LLM-based applications is preferred.
Proficiency in Python.
Familiarity with machine learning libraries and frameworks such as Scikit-learn, TensorFlow, or PyTorch.
Basic understanding of machine learning, neural networks, deep learning, NLP fundamentals, and transformer-based models. Basic understanding of REST APIs and backend integration.
Experience using Git for version control.
Basic knowledge of Docker, cloud platforms (AWS, GCP, or Azure), Linux environments, and MLOps fundamentals is a plus.
Understanding of evaluation metrics such as accuracy, precision, recall, and F1-score.
Exposure to LangChain, RAG pipelines, LLM application development, model optimization techniques, or participation in Kaggle or similar competitions is a plus.

Responsibilities:

Assist in designing, developing, testing, and implementing machine learning and deep learning models for structured and unstructured data under senior guidance.
Support NLP tasks, including text classification, basic named entity recognition (NER), chatbot features, and fine-tuning pre-trained and transformer-based models.
Assist in building and maintaining Retrieval-Augmented Generation (RAG) pipelines and support LoRA-based fine-tuning when required.
Perform data cleaning, preprocessing, feature engineering, and exploratory data analysis (EDA) to prepare datasets for training and evaluation.
Conduct experiments, evaluate model performance using standard metrics, optimize basic hyperparameters, and document results.
Assist in integrating large language model (LLM) APIs into applications and support prompt engineering and testing using frameworks such as LangChain or similar tools.
Assist in deploying machine learning models using REST APIs (FastAPI or Flask), support Docker-based containerization, and contribute to CI/CD pipelines and version control using Git.
Monitor model performance in staging and production and support dataset and model versioning.
Assist in developing and evaluating computer vision models, including image classification, object detection, and segmentation, and support image preprocessing.
Document code, models, and workflows, and collaborate with data engineers, backend developers, and product teams.
Stay updated with machine learning, LLM, and AI advancements and continuously improve technical skills.

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