AI / LLM Engineer
AI / LLM Engineer
kaleidofin private limited- Posted 2 months ago
- Over 100 applicants have applied
Job Description
The Core Responsibilities For The Job Include The Following
AI and LLM Engineering:
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.
More Info
Key Skills
scikit-learn
Hugging Face Transformers
LLM prompt engineering
vector embeddings
RAG pipelines
OpenAI API
knowledge retrieval
About Company
Skills:
Java, Typescript, Javascript, Automated Testing, Cursor, Python, Azure DevOps, GitHub Copilot, Microsoft Copilot, Claude Code, Amazon Q Developer
Skills:
Pyspark, Apache Spark, Data Warehousing, Kafka, Dimensional Modeling, Sql, ELT, Apache Airflow, Git, Gcp, Terraform, Docker, Databricks, Azure, Python, Kubernetes, AWS, Etl, Apache Iceberg, Generative AI, Delta Lake, Apache Hudi, Large Language Models
Skills:
Pytest, Sql, Nosql, React, Database Modeling, FastAPI, Python, AWS Bedrock, event-driven patterns, Claude Code, Copilot, REST API design, async, LLM APIs, Pydantic
Skills:
graph databases , Predictive Analytics, Python Programming, LangChain, agentic AI solutions, embeddings, data pipelines, Transformers, cloud-native environments, KPI modeling, vector databases, AutoGen, LangGraph, LLM RAG, telecom operations, ML fundamentals, MCP, LLM architectures, semantic search, LlamaIndex, AIOps
Skills:
bayesian inference , Sentiment Analysis, Tensorflow, Pytorch, Python, ML algorithms, LLMs, transfer learning, Hugging Face, text generation, language modeling, reinforcement learning, transformer architecture, R, Julia, Fine Tuning, Gen AI libraries, neural network frameworks, NLP techniques, OpenAI, RAG pipelines, NER