ML Engineer

As a Machine Learning Engineer at our company, you will play a crucial role in designing, building, and deploying scalable ML models and end-to-end AI solutions. You will have the opportunity to work on cutting-edge AI/ML and LLM-based projects in a collaborative and growth-driven environment. Here's a breakdown of what your responsibilities will entail: - Develop and implement machine learning models for structured and unstructured data. - Perform data preprocessing, feature engineering, and exploratory data analysis using Pandas and NumPy. - Design and maintain end-to-end ML pipelines for training, validation, deployment, and monitoring. - Apply and fine-tune ML algorithms using Scikit-learn, TensorFlow, and PyTorch. - Utilize PySpark for large-scale data processing and distributed ML workloads. - Implement and manage model deployment using AWS SageMaker, Azure ML, or GCP Vertex AI. - Use MLflow or similar tools for experiment tracking, versioning, and reproducibility. - Monitor and optimize models for performance, drift, and scalability in production environments. - Work with Large Language Models (LLMs) such as OpenAI GPT and Hugging Face Transformers for advanced NLP and generative AI use cases. - Collaborate with Data Scientists, Engineers, and Product teams to integrate ML solutions into production systems. - Contribute to MLOps practices, ensuring automation and efficiency across the model lifecycle. - Stay up-to-date with emerging trends in ML, AI frameworks, and cloud-based ML solutions. To excel in this role, you should possess the following qualifications: - Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or related field. - 4-5 years of hands-on experience in Machine Learning Engineering or a similar role. - Strong programming skills in Python with proficiency in Pandas, NumPy, and Scikit-learn. - Expertise in TensorFlow, PyTorch, and PySpark. - Experience building and deploying end-to-end ML pipelines. - Strong understanding of model evaluation techniques, fine-tuning, and optimization. - Experience with MLOps tools such as MLflow, Kubeflow, or DVC. - Familiarity with OpenAI, Hugging Face Transformers, and LLM architectures. - Proficiency with cloud ML platforms like AWS SageMaker, Azure ML, or GCP Vertex AI. - Solid understanding of model lifecycle management, versioning, and experiment reproducibility. - Excellent analytical thinking, problem-solving, and communication skills. - Proven ability to work effectively in cross-functional and collaborative environments. Additionally, it would be beneficial if you have experience with data versioning tools, containerization, orchestration tools, and generative AI applications. Don't miss the opportunity to work remotely from Pune, Maharashtra, and enjoy competitive compensation along with professional development opportunities while having access to the latest AI tools and cloud ML infrastructure.,