AI / LLM 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 & LLM skills are a must-have.
• Hands-on experience or strong project exposure to LLM 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 are a must-have.
• Strong Python programming skills for 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 normalizing 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.
• Clear understanding of 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 & 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 optimization.

Nice to Have:

• Experience in fintech, credit scoring, risk analytics, or financial inclusion domains.
• Experience with Machine Learning algorithms.
• 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 & 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.