AI / ML Engineer

Responsibilities

• Design, develop, and deploy machine learning models for classification, regression, clustering, recommendations, or NLP tasks.
• Clean, preprocess, and analyse large datasets to extract meaningful insights and features.
• Work closely with data engineers to develop scalable and reliable data pipelines.
• Experiment with different algorithms and techniques to improve model performance.
• Monitor and maintain production ML models, including retraining and model drift detection.
• Collaborate with software engineers to integrate ML models into applications and services.
• Document processes, experiments, and decisions for reproducibility and transparency.
• Stay current with the latest research and trends in machine learning and AI.
  

Requirements

• 1-3 years of hands-on experience in building and deploying ML models in real-world applications.
• Strong knowledge of Python and ML libraries such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar.
• Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Solid understanding of ML concepts such as supervised and unsupervised learning, overfitting, regularisation, etc.
• Experience working with Jupyter, pandas, NumPy, and visualisation libraries like Matplotlib or Seaborn.
• Familiarity with version control (Git) and basic software engineering practices.
• You consistently demonstrate strong verbal and written communication skills as well as strong analytical and problem-solving abilities
• You should have a master's degree /bachelor's (BS) in computer science, Software Engineering, IT, Technology Management or related degrees and throughout education in English medium.
• Have knowledge of cloud platforms (AWS, Azure, GCP) and ML services (SageMaker, Vertex AI, etc. )
• Have knowledge of GenAI prompting and hosting of LLMs.
• Have experience with NLP libraries (spaCy, Hugging Face Transformers, NLTK).
• Have familiarity with MLOps tools and practices (MLflow, DVC, Kubeflow, etc. ).
• Have exposure to deep learning and neural network architectures.
• Have knowledge of REST APIs and how to serve ML models (e. g., Flask, FastAPI, Docker).