The GenAI & Data Science Engineer is a seasoned professional role. Applies in-depth disciplinary knowledge, contributing to the development of new techniques and the improvement of processes and work-flow for the area or function. Integrates subject matter and industry expertise within a Gen AI and Data Science area. Requires in-depth understanding of application of Gen AI / Agentic AI as well as coordinate and contribute to the objectives of the function and overall business. Evaluates moderately complex and variable issues with substantial potential impact, where development of an approach/taking of an action involves weighing various alternatives and balancing potentially conflicting situations using multiple sources of information. Requires good analytical skills in order to filter, prioritize and validate potentially complex and dynamic material from multiple sources. Excellent communication and diplomacy skills are required. Regularly assumes informal/formal leadership role within teams. Involved in coaching and training of new recruits. Significant impact in terms of project size, geography, etc. by influencing decisions through advice, counsel and/or facilitating services to others in area of specialization. Work and performance of all teams in the area are directly affected by the performance of the individual.
Responsibilities:
- Design, Develop and deploy generative models for various applications in Fraud Operations.
- RAG Frameworks – Able to customize and fine-tune existing RAG frameworks or design new RAG to meet project requirements.
- Design and Develop POC / full scale solutions on various automations in Operations area including Conversational AI, Muti Agent Systems for multiple services.
- Responsible for development and deployment of Machine Learning / Deep Learning based models
- Collaborate with cross-functional team to understand business requirements and translate them into AI solutions.
- Conduct research to advance the state-of-the art in generative modeling and stay up to date with latest advancements in the field.
- Optimize and fine-tune models for performance, scalability, and robustness.
- Implement and maintain AI pipelines and infrastructure to support model training and deployment.
- Perform data analysis and preprocessing to ensure high-quality input for model training.
- Implement and maintain AI pipelines and infrastructure to support model training and deployment. - Perform data analysis and preprocessing to ensure high-quality input for model training.
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Digital Software Engineering ------------------------------------------------------
Time Type:
Full time ------------------------------------------------------
Most Relevant Skills
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Other Relevant Skills
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