Data-Driven Artificial Intelligence Systems Engineer

1 week ago

nellore, India beBeeMachineLearning Full time

We're seeking a visionary Machine Learning Engineer to spearhead the development of cutting-edge AI systems. As a key member of our team, you'll leverage your expertise in machine learning and programming to drive innovation and improve our predictive automation capabilities.Key Responsibilities:Design and develop efficient machine learning models for various applicationsCollaborate with data scientists, software engineers, and product managers to craft data-driven featuresOptimize and enhance the performance of existing machine learning modelsImplement and maintain scalable machine learning pipelines and infrastructureAnalyze and preprocess large datasets to extract valuable insights and featuresStay abreast of the latest advancements in machine learning, deep learning, and related technologiesConduct model training, validation, and performance evaluation to ensure models meet required accuracy and reliability standardsCreate and maintain documentation related to machine learning models, algorithms, and processesRequirements:Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field3+ years of experience in AI and Machine Learning1+ years of experience in General AIProven experience as a Machine Learning Engineer, Data Scientist, or in a similar roleStrong programming skills in Python, R, or similar languagesProficiency 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 visualizationHands-on experience with SQL databases and API integrationExperience with text generation techniques, including language models like GPTHands-on experience with cloud platforms (AWS, GCP, or Azure) and deploying models in productionSolid understanding of machine learning algorithms, deep learning architectures, and statistical methodsExperience with version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) pipelinesNice-to-Have:Knowledge of natural language processing (NLP) and its applicationsExperience with MLOps tools and best practices for scalable model deployment and monitoringFamiliarity with data privacy and security regulationsExperience with real-time data processing and streaming technologiesExperience with reinforcement learning, generative models, or unsupervised learning techniques

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