AI Engineer

Requirements:

• 0-2 years of industry or project experience in AI/ML engineering (internships and academic projects strongly count).
• Bachelor's OR master's degree in statistics, computer science, Engineering, Mathematics, or a related technical field.

AI and LLM Skills (Must Have):

• 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:
• Supervised Learning: Linear Regression, Logistic Regression, SVM, Decision Trees, Random Forest, XGBoost.
• Neural Networks: Shallow Neural Networks and familiarity with deep learning architectures.
• Unsupervised Learning: Clustering (K-Means, DBSCAN), Recommender Systems.
• Time Series and Anomaly Detection: ARIMA, Isolation Forest, statistical anomaly methods.
• Strong command of model selection, cross-validation, feature selection, and ensemble methods (boosting, bagging, stacking).
• 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.