Citi is hiring AI/ML Analysts to join its Specialized Analytics team in Haryana. This opportunity is ideal for candidates interested in Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG). As part of a global financial organization, you'll contribute to developing intelligent AI solutions, improving model performance, preparing enterprise-scale datasets, and collaborating with cross-functional teams to integrate AI into real-world business applications. This role offers excellent exposure to production AI systems, cloud technologies, and modern machine learning workflows.
Working at Citi provides exposure to enterprise-scale artificial intelligence projects where machine learning models directly support business decisions. This position is suitable for candidates who enjoy solving analytical problems, experimenting with AI models, and continuously learning new technologies in the rapidly evolving field of Generative AI.
🤖 What You'll Be Working On
As an AI/ML Analyst, you'll support the complete lifecycle of modern AI model development. Your responsibilities extend beyond training models—you'll also help prepare datasets, evaluate model performance, optimize inference quality, and collaborate with engineering teams for deployment.
Key responsibilities include:
Assisting in developing and fine-tuning Generative AI and LLM models.
Preparing, cleaning, and organizing datasets for model training.
Customizing and improving Retrieval-Augmented Generation (RAG) frameworks.
Conducting experiments to evaluate model quality.
Improving scalability, efficiency, and prediction accuracy.
Supporting deployment of AI models into production applications.
Maintaining technical documentation and experiment results.
🧠 Technologies You'll Use
This position provides hands-on experience with several modern AI technologies.
Python
TensorFlow
PyTorch
Hugging Face Transformers
Git
AWS
Azure
Google Cloud
Candidates interested in enterprise AI engineering will gain valuable exposure to production-ready machine learning workflows used within global financial organizations.
📚 Technical Knowledge That Matters
The hiring team is looking for candidates with strong technical fundamentals rather than expertise in a single framework.
Important concepts include:
Machine Learning fundamentals
Large Language Models (LLMs)
Natural Language Processing (NLP)
Tokenization techniques
Word embeddings
Sequence models
Linear Algebra
Calculus
Probability and Statistics
Model evaluation metrics
Data preprocessing techniques
Strong mathematical fundamentals often make learning advanced AI frameworks much easier than relying only on coding experience.
🌐 Enterprise Collaboration
This role involves working alongside data scientists, software engineers, analytics teams, and business stakeholders. You'll participate in collaborative AI projects where communication is just as valuable as technical ability.
The team values individuals who can explain technical findings clearly, document experiments, and contribute effectively within cross-functional environments.
🚀 Skills That Can Strengthen Your Profile
Candidates may stand out if they have experience with:
Personal AI or ML projects
Internship experience
Kaggle competitions
GitHub portfolios
Cloud certifications
Open-source contributions
Model deployment projects
Prompt engineering
Vector databases
RAG pipelines
Hands-on AI projects are highly valuable during resume screening.
🎓 Educational Qualification
Applicants should possess:
Bachelor's degree in Computer Science, Data Science, Electrical Engineering, or a related discipline.
Master's degree is considered an advantage but is not mandatory.
Fresh graduates with relevant academic projects are encouraged to apply.
📈 Career Growth Opportunities
Working in Citi's analytics division provides exposure to modern enterprise AI systems and financial analytics. The experience gained in this role can open future opportunities in:
Machine Learning Engineering
AI Research
Data Science
NLP Engineering
Generative AI Development
MLOps Engineering
AI Platform Engineering
Advanced Analytics
The role also helps build experience with enterprise deployment practices, production monitoring, documentation standards, and scalable AI infrastructure.
📝 Interview Preparation Tips
Candidates preparing for interviews should revise:
Python programming
Object-Oriented Programming
SQL basics
Machine Learning algorithms
NLP fundamentals
Transformer architecture
Retrieval-Augmented Generation (RAG)
Hugging Face ecosystem
Statistics
Probability
Cloud computing basics
Git workflows
Model evaluation techniques
Prompt engineering concepts
Building one complete AI project and publishing it on GitHub can significantly strengthen your resume.
🔑 Keywords for Resume
Python • Machine Learning • Deep Learning • Generative AI • Large Language Models • LLM • NLP • Retrieval-Augmented Generation • RAG • TensorFlow • PyTorch • Hugging Face • Data Preprocessing • Model Evaluation • Statistics • Linear Algebra • Probability • Git • AWS • Azure • Google Cloud • MLOps • Prompt Engineering • AI Research • Data Science • Model Deployment • Analytical Thinking • Problem Solving • Cross Functional Collaboration
💡 Final Thoughts
This AI/ML Analyst opportunity at Citi offers candidates the chance to work on enterprise-scale Generative AI initiatives while developing expertise in LLMs, RAG frameworks, NLP, and production machine learning systems. It is well suited for graduates and early-career professionals looking to build a long-term career in Artificial Intelligence within a global financial technology environment.
The above article is written by me, a person interested in technology, automobiles, modern gadgets, movies, music, and clean aesthetics.
