Machine Learning Engineer (ML Engineer)

Job Description

Role Description: This is a full-time on-site role as a Machine Learning Engineer in Chennai. The Machine Learning Engineer will be responsible for pattern recognition, developing neural networks, applying statistical analysis, and implementing algorithms on a day-to-day basis. Responsibilities: Develop and deploy machine learning models: Design, build, train, and evaluate various models, including deep learning, natural language processing, and time series analysis models, leveraging your understanding of core ML concepts like supervised, unsupervised, and reinforcement learning paradigms. This includes experience with Transformer models and their applications. Data processing and feature engineering: Clean, transform, and prepare large datasets for model training and evaluation. Develop innovative feature engineering techniques to improve model performance. LLM and RAG development: Build and fine-tune LLMs for specific tasks related to voice and data analytics. Implement RAG systems to enhance LLM performance with external knowledge sources. Prompt-to-SQL generation: Develop models that can translate natural language queries into SQL queries, enabling users to easily access and analyze data. Data insights and visualization: Develop algorithms and tools to extract meaningful insights from data and present them clearly and concisely. Classifier development: Build and train classifiers for various tasks, such as sentiment analysis, topic classification, and intent recognition. This includes understanding different classification algorithms (e.g., logistic regression, support vector machines, decision trees) and performance metrics (e.g., precision, recall, F1- score). Model optimization and deployment: Optimize model performance for speed and accuracy. Deploy models to production environments using appropriate tools and technologies. Stay up-to-date: Keep abreast of the latest advancements in machine learning and related fields. Explore and experiment with new techniques and technologies. Collaborate effectively: Work closely with other engineers, product managers, and researchers to deliver high-quality solutions. Requirements: 2-4 years of experience developing and deploying machine learning models in a production environment. Strong understanding of fundamental machine learning concepts, including model selection, evaluation metrics, bias-variance tradeoffs, and regularization techniques. Solid grasp of neural networks, including different architectures (e.g., CNNs, RNNs, Transformers), activation functions, backpropagation, and optimization algorithms. Deep understanding of Transformer models, including attention mechanisms, selfattention, and different Transformer variants (e.g., BERT, GPT). Experience with classification modelling techniques, including handling imbalanced datasets, multi-class classification, and model interpretation. Strong programming skills in Python and experience with relevant libraries such as TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers. Specifically, experience fine-tuning and deploying Transformer models using these libraries. Nice to Have: Experience with cloud platforms such as AWS, Azure, or GCP, including deploying and managing Transformer models at scale. Experience with database technologies such as SQL and NoSQL. Experience with version control systems such as Git. Excellent communication and collaboration skills. Experience with LLMs, RAG, and prompt engineering, specifically using Transformerbased models, is a strong plus. Experience with voice data processing and analysis is a plus