Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips - the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world - like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world.

What We Offer

Location:

Bangalore,IND

You ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits .

Educational Qualifications:

B.E./B.Tech/M.Tech/MCA in Computer Science, Electronics & Communication, Electrical Engineering, or a related field.
A master s degree in Computer Science, Artificial Intelligence, or a specialization in Deep Learning or Natural Language Processing is highly preferred.

Experience:

4 - 7 years of IT experience, with a minimum of 3+ years in data science.
Proven experience in designing, pretraining, and fine-tuning large-scale deep learning models, including Large Language Models (LLMs) for NLP tasks such as text generation, summarization, and question answering.

Technical Skills:

Strong programming skills in Python, with hands-on experience in deep learning frameworks like TensorFlow, PyTorch, or Keras.
Familiarity with Databricks notebooks, MLflow, and Delta Lake for scalable machine learning workflows.
Experience with MLOps best practices, including model versioning, CI/CD pipelines, and automated deployment.
Proficiency in data preprocessing, augmentation, and handling large-scale datasets (text, image, or multimodal).
Solid understanding of transformer architectures (e.g., BERT, GPT, LLaMA) and techniques for pretraining and fine-tuning LLMs.
Knowledge of prompt engineering, parameter-efficient fine-tuning (LoRA, adapters), and evaluation of generative models.
Exposure to computer vision or multimodal models is a plus.

Cloud & Infrastructure:

Strong expertise in Azure cloud ecosystem.
Experience working in UNIX/Linux environments and using command-line tools for automation and scripting.

Soft Skills:

Strong analytical and problem-solving abilities.
Excellent communication skills interpersonal, written, visual (presentations), and verbal.
Ability to collaborate effectively across cross-functional teams and mentor junior team members.

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