AI/ML Engineer

Core Responsibilities

• Design, develop, and train custom machine learning models and deep learning architectures.
• Build and maintain scalable data pipelines (ETL) to preprocess large-scale structured and unstructured datasets.
• Deploy ML models into production environments and monitor performance, latency, and predictive accuracy.
• Optimize and fine-tune existing algorithms to improve computational and execution efficiency.
• Collaborate with software engineering teams to integrate AI/ML components seamlessly into core application frameworks.
• Stay updated with the latest advancements in LLMs, computer vision, or NLP technologies to drive continuous innovation.

Required Skills & Technical Toolkit

• Programming Languages: Strong proficiency in Python, R, or Java, with deep knowledge of OOP principles.
• ML/DL Frameworks: Hands-on experience with frameworks such as PyTorch, TensorFlow, Scikit-Learn, or Keras.
• Data Architecture: Solid understanding of SQL/NoSQL databases, big data frameworks (Spark, Hadoop), and data manipulation libraries (Pandas, NumPy).
• Cloud Infrastructure: Familiarity with deploying ML models on cloud environments (AWS, Azure, or Google Cloud Platform) utilizing tools like SageMaker, Docker, or Kubernetes.
• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related engineering field.