Intelligent Automation Specialist

1 week ago

vijayawada, India beBeeMachineLearning Full time

Machine Learning Engineer Overview: We are seeking a highly skilled Machine Learning Engineer to develop efficient, data-driven artificial intelligence systems that advance our predictive automation capabilities. The ideal candidate should have a strong background in machine learning, data science, and software development, with the ability to work closely with our product, data science, and engineering teams to develop, deploy, and maintain machine learning models that drive our vision. Responsibilities: Designing, developing, testing, and deploying machine learning models for various applications. Collaborating with data scientists, software engineers, and product managers to develop data-driven features. Optimizing and improving the performance of existing machine learning models. Implementing and maintaining scalable machine learning pipelines and infrastructure. Analyzing and preprocessing large datasets to extract valuable insights and features. Staying updated with the latest developments in machine learning, deep learning, and related technologies. Conducting model training, validation, and performance evaluation to ensure models meet the required accuracy and reliability. Creating and maintaining documentation related to machine learning models, algorithms, and processes. Developing A/B testing frameworks and managing the deployment of models in production. Qualifications: Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field. 3+ years of experience in AI and machine learning. 1+ years of experience in General AI. Proven experience as a Machine Learning Engineer, Data Scientist, or in a similar role. Strong programming skills in Python, R, or similar languages. Proficiency with machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, etc., and experience with NLP libraries (e.g., NLTK, spaCy, Hugging Face Transformers). Experience with data preprocessing, data wrangling, and data visualization. Hands-on experience with SQL databases and API integration. Experience with text generation techniques, including language models like GPT. Hands-on experience with cloud platforms (AWS, GCP, or Azure) and deploying models in production. Solid understanding of machine learning algorithms, deep learning architectures, and statistical methods. Experience with version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) pipelines. Ability to work in a collaborative environment and communicate effectively with cross-functional teams. Nice-to-Have: Knowledge of natural language processing (NLP) and its applications. Experience with MLOps tools and best practices for scalable model deployment and monitoring. Familiarity with data privacy and security regulations. Experience with real-time data processing and streaming technologies. Experience with reinforcement learning, generative models, or unsupervised learning techniques.

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