AI LLM Engineer

The Core Responsibilities For The Job Include The Following

AI and LLM Engineering:

• Design and develop LLM-powered features, including RAG (Retrieval-Augmented Generation) pipelines and prompt engineering.
• Build and evaluate agentic AI systems using frameworks.
• Integrate vector databases for semantic search and knowledge retrieval.
• Explore self-hosted LLM deployments using Ollama, vLLM, or similar frameworks.
  
  

• Build, train, and evaluate ML models for credit risk, fraud detection, anomaly detection, and customer segmentation.
• Perform feature engineering, hyperparameter tuning, model selection, and error analysis.
• Apply ensemble methods (boosting, bagging, stacking) to improve model robustness.
• Conduct A/B testing, multivariate experiments, and statistical analysis to validate model performance.
  
  

• Build and maintain scalable ML pipelines and data workflows on cloud infrastructure.
• Collaborate with data engineers to integrate models into production systems using MLOps best practices.
• Write clean, production-level Python code and contribute to shared AI tooling and libraries.
  
  

• 1-5 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.
  
  

• 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.
  
  

• Strong Python programming skills and clean, maintainable, production-ready code.
• Proficient in ML libraries: scikit-learn, TensorFlow or PyTorch, XGBoost, pandas, and 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, coding, implementation, error analysis, and model tuning across:
• Supervised Learning: Linear Regression, Logistic Regression, SVM, Decision Trees, Random Forest, and 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 optimization.
  
  

• 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 & Biases, or other experiment tracking tools.
  
  

• 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.