- Hands-on experience or strong project exposure to LLMs, prompt engineering, and RAG pipelines.

- Familiarity with Hugging Face Transformers, OpenAI API, or equivalent LLM frameworks.

- Understanding of vector embeddings, semantic search, and knowledge retrieval concepts.

- Awareness of GenAI and Agentic AI methodologies and their practical applications.

Programming / Cloud / Data Skills (Must Have) :

- Strong Python programming skills; clean, maintainable, production-ready code.

- Proficient in ML libraries : scikit-learn, TensorFlow or PyTorch, XGBoost, pandas, NumPy.

- Solid SQL skills for data querying, transformation, and mining structured datasets.

- Experience normalising and preprocessing data for consistency, quality, and model readiness.

- Working knowledge of at least one major cloud platform : AWS, GCP, or Azure.

- Understanding of cloud storage, compute, and containerization basics (Docker, Kubernetes).

- Exposure to big data tools such as Spark or Hadoop (MapReduce, Hive, Pig) is a plus.

Machine Learning Algorithms (Good to Have) :

- Clear understanding, coding, implementation, error analysis, and model tuning across :

1. Supervised Learning : Linear Regression, Logistic Regression, SVM, Decision Trees, Random Forest, XGBoost.

2. Neural Networks : Shallow Neural Networks and familiarity with deep learning architectures.

3. Unsupervised Learning : Clustering (K-Means, DBSCAN), Recommender Systems.

4. Time Series and Anomaly Detection : ARIMA, Isolation Forest, statistical anomaly methods.

5. Strong command of model selection, cross-validation, feature selection, and ensemble methods (boosting, bagging, stacking).

6. Ability to perform hyperparameter tuning using Grid Search, Random Search, or Bayesian optimisation.

Nice to Have :

- Experience in fintech, credit scoring, risk analytics, or financial inclusion domains.

- Contributions to open-source ML/AI projects or a strong personal project portfolio on GitHub.

- Familiarity with MCP (Model Context Protocol) or building AI tool integrations.

- Experience with MLflow, Weights and Biases, or other experiment tracking tools.

Soft Skills :

- Genuine curiosity about AI/ML and eagerness to learn in a fast-moving field.

- Strong problem-solving mindset, able to break down complex challenges into actionable steps.

- Clear communication skills to present model results and insights to non-technical stakeholders.

- Collaborative team player who thrives in a cross-functional, mission-driven environment.