Responsible AI Engineer

As a Responsible AI Engineer, you will be pivotal in assessing AI systems to ensure they adhere to ethical, responsible, and sustainable practices. You will design and implement technology mitigation strategies to achieve these standards.

Key Responsibilities

1. NLP Application Development. Design and develop applications and systems utilizing Natural Language Processing (NLP) techniques, including deep learning, neural networks, and chatbots.
2. Algorithm Optimization. Implement and optimize NLP algorithms and models to enhance performance and accuracy.
3. Collaborative Problem Solving. Work with cross-functional teams to translate business requirements into technical solutions.
4. Advancement Integration. Stay current with advancements in NLP and AI technologies, integrating innovative approaches for competitive advantage.
5. Data Communication. Effectively communicate technical findings to stakeholders, using data visualization tools for clarity.
6. Model Building. Build predictive models and develop advanced algorithms for extracting and classifying information from large datasets.
7. Technology Evaluation. Evaluate emerging technologies for their potential contributions to our analytical platform and identify new data patterns using various techniques.
8. Tool Proficiency. Utilize ML data mining toolkits and information retrieval libraries, including NLP, Semantic Web, R, Core NLP, NLTK, Lucene, and SOLR.

Must-Have Skills

1. Natural Language Processing (NLP). Extensive experience with NLP algorithms and models.
2. Machine Learning Algorithms. Strong understanding of machine learning algorithms such as CRFs, SVM, RNN, and LSTM.
3. Technical Expertise. Proficiency in Python and familiarity with machine learning and NLP toolkits and libraries.
4. Analytical Skills. Solid understanding of machine learning algorithms, statistical analysis, and the ability to project both business and technological benefits.
5. Communication. Excellent written, verbal, and interpersonal communication skills with the ability to present technical findings clearly.

Good to Have Skills

• Experience with NLP application areas such as Semantic Web and Ontologies, Machine Translation, Sentiment Analysis, Document Classification, Question Answer Matching, and Text Summarization.
• Proven experience in creating NLP pipelines for processing large document corpora.
• Educational Qualification. 15 years of full-time education, ideally in Computer Science, Mathematics, or a related field.
• Experience.Minimum 3 years of professional experience in AI/ML with a focus on NLP.

Additional Information

• The ideal candidate will be a self-starter with a strong problem-solving ability and a proven track record of delivering impactful data-driven solutions.
• Must possess strong problem-solving skills, the ability to methodically analyze and resolve technical challenges, and effective communication skills.

Application Process

To apply for this role, please submit your resume and a cover letter outlining your experience and qualifications related to the responsibilities and skills mentioned above.