Machine Learning Architect

5 days ago


Pune, Maharashtra, India JetSynthesys Full time

**Job Title:** Machine Learning Architect

We are seeking an experienced Machine Learning Architect to join our team at JetSynthesys in Pune. As a key member of our technology team, you will be responsible for designing and implementing machine learning solutions using large language models (LLMs).

**Salary:** ₹30,00,000 - ₹40,00,000 per annum

**Job Description:**

The ideal candidate will have a strong background in machine learning, NLP, and artificial intelligence with hands-on experience in deploying and fine-tuning LLMs. Responsibilities include:

• Designing and optimizing machine learning solutions using LLMs to solve complex problems.

• Collaborating with data scientists and software engineers to integrate machine learning capabilities into the product.

• Developing scalable machine learning pipelines for training, testing, and deploying models.

• Analyzing and improving the performance of models by experimenting with different architectures, hyperparameters, and datasets.

• Staying updated with the latest advancements in machine learning, NLP, and AI technologies and applying them to enhance model performance.

• Troubleshooting and resolving issues related to model accuracy, latency, and scalability.

Education Requirements:

• Bachelor's or master's degree in computer science, engineering, or a related field.

• Preferred: Publications or contributions to research papers in the field of machine learning or NLP.

• Experience in working on multilingual models or domain-specific machine learning models.

• Strong experience with machine learning, deep learning, and NLP frameworks like TensorFlow, PyTorch, Hugging Face, etc.

• Expertise in working with large pre-trained models such as GPT, BERT, T5, etc.

• Proficient in Python and libraries such as NumPy, pandas, and Scikit-learn.

• Familiarity with distributed systems and cloud platforms like AWS, Azure, or Google Cloud for model deployment.

• Experience in fine-tuning, training, and deploying models in a production environment.

• Knowledge of data engineering practices to manage large-scale data pipelines for model training and inference.

• Strong analytical and problem-solving skills, with the ability to work in a fast-paced environment.



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