Machine Learning Engineer

Industry

Overview

Machine Learning / Deep Learning Engineer

Key Responsibilities

• Design and develop machine learning and deep learning solutions for advanced metrology and semiconductor applications.
• Build, train, and optimize ML/DL models for image processing and data-driven insights.
• Develop, test, and deploy algorithms using frameworks such as PyTorch, TensorFlow, or Keras.
• Work with Python and its scientific ecosystem (NumPy, Pandas, Scikit-learn) for data manipulation and prototyping.
• Apply supervised and unsupervised learning techniques, including regularization, feature engineering, model selection, and ensemble methods (e.g., XGBoost, LightGBM).
• Implement deep learning architectures such as CNNs, RNNs, LSTMs, Transformers, and Attention mechanisms.
• Utilize vector databases (e.g., Milvus, FAISS, Pinecone, ElasticSearch) for information retrieval and LLM-driven applications.
• Contribute to prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning large language models using frameworks like Hugging Face Transformers.
• Work with data tools and platforms such as SQL, Git/GitHub, Docker, and Jupyter notebooks.
• Collaborate with cross-functional teams to translate technical requirements into scalable, production-ready AI solutions.
• Document algorithms, architecture, and processes while contributing to continuous system improvements.
  

Required Qualifications

• Education:
• Doctorate degree with at least 2 years of relevant experience, or
• Master’s degree with 5 years of experience, or
• Bachelor’s degree with 7 years of experience in building AI systems and solutions using Machine Learning, Deep Learning, and LLMs.
• Preference for candidates from Tier-1 or premier institutes (IIT, IISc, IIIT, NIT, BITS, or equivalent).
• Core Experience:
• Minimum 5 years of experience in Python, Machine Learning, Deep Learning, Algorithm Development, and Image Processing.
• Minimum 3.5 to 4 years of hands-on proficiency with PyTorch or TensorFlow/Keras.
• Strong foundation in data structures, algorithms, and object-oriented programming.
• Solid understanding of software development best practices and performance optimization.
• Proven experience working in product-based, semiconductor, or hardware manufacturing companies (current or past).
• Consistent career progression with no significant employment gaps.