Natural Language Processing Engineer

Job Description

Role & responsibilities Preferred candidate profile Project Role : Responsible AI Engineer Project Role Description : Assess AI systems for adherence to predefined thresholds and benchmarks related to responsible, ethical and sustainable practices. Design and implement technology mitigation strategies for systems to ensure ethical and responsible standards are achieved. Must have skills : Natural Language Processing (NLP) Good to have skills : NA Minimum 5+ year(s) of experience is required Educational Qualification : 15 years full time education Summary: As a Responsible AI Engineer, you will assess AI systems for adherence to predefined thresholds and benchmarks related to responsible, ethical, and sustainable practices. You will design and implement technology mitigation strategies for systems to ensure ethical and responsible standards are achieved. Your typical day will involve evaluating AI systems, identifying areas of improvement, and implementing strategies to enhance ethical and responsible practices. Roles & Responsibilities: - Expected to be an SME, collaborate and manage the team to perform. - Responsible for team decisions. - Engage with multiple teams and contribute on key decisions. - Provide solutions to problems for their immediate team and across multiple teams. - Ensure adherence to predefined thresholds and benchmarks related to responsible, ethical, and sustainable practices. - Design and implement technology mitigation strategies for systems to ensure ethical and responsible standards are achieved. - Evaluate AI systems and identify areas of improvement. - Implement strategies to enhance ethical and responsible practices. Professional & Technical Skills: - Must To Have Skills: Proficiency in Natural Language Processing (NLP). - Strong understanding of statistical analysis and machine learning algorithms. - Experience with data visualization tools such as Tableau or Power BI. - Hands-on implementing various machine learning algorithms such as linear regression, logistic regression, decision trees, and clustering algorithms. - Solid grasp of data munging techniques, including data cleaning, transformation, and normalization to ensure data quality and integrity. Perks and benefits