AVP - Gen AI - Fraud Analytics DS - C12 - PUNE

The Specialized Analytics Sr Anlst 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 defined area. Requires in-depth understanding of how areas collectively integrate within the sub-function 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 AI based solutions for various Fraud prevention area.
• RAG Frameworks – Able to customize and fine-tune existing RAG frameworks or design new RAG to meet project requirements. 
• 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.
• Strong experience in prompt engineering
• 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.
• Mentor junior team members and provide technical guidance. 
• Write and maintain comprehensive documentation for models and algorithms.
• Present findings and project progress to stakeholders and management.
• Experience with cloud platforms such as AWS, Google Cloud, or Azure. - Knowledge of reinforcement learning and its applications in generative AI.
• Familiarity with MLOps practices and tools.
• Contributions to open-source projects or published research in top-tier conferences/journals. 

Qualifications:

• 8+ years of experience in machine learning and deep learning, with a focus on generative models. 
• Strong proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras. 
• Experience working with Model Risk Management team for model approval and governance related work.
• Experience with natural language processing (NLP) and natural language generation (NLG). 
• Proven track record of building and deploying generative models-based solutions in production environments particularly using RAG frameworks, few shot prompts, etc.
• Solid understanding of machine learning algorithms, data structures, and software engineering principles. 
• Excellent problem-solving skills and the ability to work independently and as part of a team.
  

Education:

• Bachelor’s or master’s degree in computer science, Data Science, Machine Learning, or a related field. A Ph.D. is a plus.

This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.

Preferred Skills –

• Experience with cloud platforms such as AWS, Google Cloud, or Azure. - Knowledge of reinforcement learning and its applications in generative AI.
• Familiarity with MLOps practices and tools.

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