Machine Learning Engineer
1 month ago
About Us
LC Adroit Vantage, a Wealth Tech firm, is revolutionizing the industry by digitizing major touchpoints within the Wealth Management client journey. Our mission is to set standards for Wealth-Tech by providing a fully integrated Platform as a Service (PaaS) that enriches wholesome investment management capabilities.
Key Responsibilities
- Collaborate with domain experts and stakeholders to understand business challenges and requirements to be supported by NLP/ML.
- Develop and implement machine learning models and algorithms using NLP techniques, such as text classification, sentiment analysis, named entity recognition, and topic modeling, to solve specific business problems.
- Perform data analysis and pre-processing tasks, including data cleaning, feature engineering, and data transformation, to prepare data for machine learning models.
- Evaluate and optimize the performance of machine learning models using appropriate metrics and techniques, and iterate on model design and implementation as needed.
- Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to integrate machine learning models into our products and services.
- Stay up-to-date with the latest advancements in machine learning, NLP, and related technologies, and proactively apply them to solve business challenges.
- Ensure compliance with data privacy and security requirements throughout the machine learning development and deployment process.
Requirements
- Bachelor's or master's degree in computer science, Machine Learning, Artificial Intelligence, or a related field.
- More than 5 years of experience in machine learning, natural language processing, and related technologies, with a strong understanding of NLP concepts and techniques.
- Hands-on experience in developing and implementing machine learning models using libraries such as TensorFlow, Keras, PyTorch, or scikit-learn.
- Proficiency in programming languages such as Python or Java, and experience with data manipulation and analysis using tools such as Pandas, NumPy, or SQL.
- Strong understanding of statistical concepts and machine learning algorithms, including supervised and unsupervised learning, feature selection, model evaluation, and model deployment.
- Experience with NLP techniques, such as text preprocessing, tokenization, POS tagging, word embeddings, and sentiment analysis.
- Strong problem-solving skills, ability to work independently and in a team, and excellent communication skills.
- Familiarity with cloud-based machine learning platforms, such as AWS, Google Cloud, or Azure, is a plus.
- Experience in working with large-scale datasets and big data technologies, such as Hadoop or Spark, is a plus.
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