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.
Machine Learning And Modeling
- 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.
Cloud And Production
- 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.
Requirements
- 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.
AI And LLM Skills (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 (Must Have)
- 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.
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, 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.
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 & 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.
This job was posted by Poornima B from Kaleidofin.
