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.
Mentor junior team members and provide technical guidance.
Write and maintain comprehensive documentation for models and algorithms.
Strong understanding of Model Risk Management (MRM) and Fair Lending (FL) guidelines for LLM based solutions
Present findings and project progress to stakeholders and management.
Recommended Qualifications:
Bachelor’s or Master’s degree in computer science, Data Science, Machine Learning, or a related field. A Ph.D. is a plus.
7+ years of experience in machine learning and deep learning, with a focus on generative AI solution design and development.
Strong proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras.
Strong background of Prompt Engineering, building agentic AI / AI Agents, API / MCPs for large scale automations in banking domain
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.
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.
Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
Education:
Bachelors/University degree or equivalent experience
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Job Family Group: Technology------------------------------------------------------
Job Family:Digital Software Engineering------------------------------------------------------
Time Type:Full time------------------------------------------------------
Most Relevant Skills Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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Skills Required
- Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field (Ph.D. a plus)
- 7+ years of experience in machine learning and deep learning, focusing on generative AI solution design and development
- Strong proficiency in Python
- Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras
- Proven track record building and deploying generative models and RAG-based solutions in production
- Experience with prompt engineering, building agentic AI/AI agents, and APIs/MCPs for large-scale automations
- Experience with NLP and NLG techniques
- Solid understanding of ML algorithms, data structures, and software engineering principles
- Strong understanding of Model Risk Management (MRM) and Fair Lending (FL) guidelines for LLM-based solutions
- Excellent communication skills and experience mentoring junior team members
Citi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Citi and has not been reviewed or approved by Citi.
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Healthcare Strength — Benefits coverage is positioned as comprehensive, including health, dental, and vision insurance plus on-site clinics, prescription drug support, and disability coverage. Family-building support such as fertility assistance is described as a notable differentiator within the overall package.
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Retirement Support — Retirement benefits are framed as strong, highlighted by a 401(k) with matching and additional plan options like a Roth 401(k). Financial support is reinforced through discounts and broader financial guidance resources tied to the benefits ecosystem.
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Wellbeing & Lifestyle Benefits — Wellbeing support extends beyond insurance through programs like an Employee Assistance Program, counseling/legal resources, and gym or wellness reimbursement. These offerings increase the perceived total rewards value even when cash compensation sentiment varies by role.
Citi Insights
What We Do
Citi's mission is to serve as a trusted partner to our clients by responsibly providing financial services that enable growth and economic progress. Our core activities are safeguarding assets, lending money, making payments and accessing the capital markets on behalf of our clients. We have 200 years of experience helping our clients meet the world's toughest challenges and embrace its greatest opportunities. We are Citi, the global bank – an institution connecting millions of people across hundreds of countries and cities.








