AI / LLM Engineer

Kaleidofin Private Limited; Bengaluru, Chennai District; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Kaleidofin Private Limited seeks an AI/ML Engineer to design and develop LLM-powered features, including RAG pipelines, and to build agentic AI systems. You will work on vector embeddings, semantic search, and production-quality Python code, collaborating with data engineers to deploy models on cloud platforms.

The role requires 1–5 years of AI/ML experience and a background in statistics, CS, engineering, or mathematics.

Qualifications

• Hands‑on experience with 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 and production‑level code practices.
• Proficiency in ML libraries: scikit‑learn, TensorFlow or PyTorch, XGBoost, pandas, and NumPy.
• Solid SQL skills for data querying, transformation, and mining.
• Exposure to cloud platforms: AWS, GCP, or Azure.

Responsibilities

• Design and develop LLM-powered features, including RAG pipelines and prompt engineering.
• Build, train, and evaluate ML models for risk, fraud, anomaly detection, and segmentation.
• Collaborate with data engineers to productionize models and follow MLOps best practices.

Skills

LLM prompt engineering

RAG pipelines

vector embeddings

semantic search

GenAI / Agentic AI

Python

ML libraries

SQL

Cloud platforms

Docker/Kubernetes

Education

Bachelor's or Master's in statistics / CS / engineering / math

Tools

Hugging Face Transformers

OpenAI API

Ollama/vLLM

Docker

Kubernetes

MLflow

Job description

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.