As a NLP Engineer - focus in LLMs, you will play a crucial role in analyzing and interpreting complex data sets to derive meaningful insights that drive informed decision-making within the organization. You will utilize your expertise in statistics, programming, and machine learning to develop and deploy data-driven models and algorithms. Your work will contribute to solving business problems, improving processes, and enhancing the overall performance of the company.
Requirements:
- Bachelor's degree in Computer Science, Engineering, or related field
- Strong programming skills in Python
- Experience with NLP libraries such as NLTK, SpaCy, or CoreNLP
- Proficient in machine learning techniques and algorithms
- Experience using open source LLMs and LangChain
- Knowledge of statistical analysis and data mining
Qualifications:
- Master's degree in Linguistics, Computational Linguistics, or related field
- Prior experience in developing NLP models and systems
- Understanding of deep learning frameworks such as TensorFlow or PyTorch
- Excellent problem-solving and analytical skills
- Strong communication and teamwork abilities
Responsibilities:
- Develop and implement natural language processing algorithms and models
- Design and build machine learning systems for language understanding and information extraction
- Analyze and process large amounts of natural language data
- Collaborate with cross-functional teams to improve NLP capabilities
- Stay up-to-date with the latest advancements in NLP and machine learning
- Data Analysis and Interpretation: Collect, clean, and preprocess data from various sources, ensuring its integrity and reliability. Perform exploratory data analysis to identify patterns, trends, and relationships within the data. Apply statistical techniques and data visualization methods to effectively communicate findings to stakeholders.
- Model Development: Design and develop predictive and prescriptive models using advanced machine learning algorithms and statistical techniques. Evaluate and select appropriate modeling approaches based on the problem at hand. Optimize models for accuracy, performance, and scalability.
- Data Mining and Pattern Recognition: Identify relevant data sources and extract valuable insights by applying data mining techniques. Utilize pattern recognition algorithms to discover hidden patterns, anomalies, and trends within large datasets. Develop strategies for feature selection and dimensionality reduction.
- Machine Learning Implementation: Build and deploy machine learning models into production systems. Collaborate with software engineers and IT teams to integrate models into existing workflows or develop new applications. Ensure the models are scalable, efficient, and maintainable
