As Text Analytics Engineer, you will primarily focus on development and deployment of Natural Language Processing (NLP) / Text Analytics applications for various business sectors within our organization. You will be working closely with data scientists, data engineers and architects on use case ideation, proof-of-concept development, and propose optimum solutions that cater to the business needs. You will work in an agile environment to help design, develop, and maintain NLP/Text Analytics models and data pipelines.

• As a core member of the NLP team, you will research, prototype, develop, deploy, and scale innovative NLP/Text Analytics/Machine Learning solutions in collaboration with other NLP Scientists, Data Engineers and Architect(s).

• You will deploy variety of Text Analytics models on large-scale datasets to address various business problems leveraging advanced NLP, machine learning, or deep learning techniques.

• Set up processes to monitor and continually improve efficiency and performance of model KPIs

• Partner with data scientists to drive NLP/machine learning modeling, and experimental design such as A/B testing.

• Software development including algorithm implementation, optimization, performance profiling, integration to production systems, testing and documentation.

• Write high-quality production code as you build and maintain robust, scalable project pipelines.

Your Profile

• Graduated with higher degree in computer science, information technology, information science, or similar fields

• 4+ years of experience in designing, developing and implementing machine learning/deep learning models (supervised or unsupervised) preferably applied to the text data.

• Strong programming experience in Python. Scala/Java is a plus.

• Familiar with commonly used NLP, machine learning, and deep learning libraries such as PyTorch, Keras, Transformers, SKLearn, Gensim, SpaCy, NLTK and so-forth.

• Experience with distributed computing with Apache Spark (PySpark).

• Good understanding of document indexing systems such as Elasticsearch, Solr or Lucene.

• Experience with version control (git), deployment of ML/NLP workflows leveraging DevOps/MLOps strategies.

• Working experience in services offered by cloud technologies. AWS is a double plus.

• Experience with deployment of dockerized applications is a double plus.

• Good verbal written communication skills