AI/ML Engineer

About the job

JD: Technical Skills – Mandatory AI & LLM Engineering: • • Agentic AI frameworks: LangGraph, LangChain Agents, multi-agent orchestration. • • RAG expertise including Standard RAG, Agentic RAG, and Graph RAG. • • LLM API integration with Azure OpenAI, OpenAI, Anthropic. • • Prompt engineering, structured outputs, and evaluation frameworks. • • Vector databases … such as Azure AI Search, Pinecone, Chroma, and Weaviate. Machine Learning (Enterprise-Level Models): • • Prophet – enterprise-grade time series forecasting. • • XGBoost – widely used for high-performance tabular ML. • • LightGBM – optimized boosting framework for large-scale training. • • CatBoost – efficient model for categorical-heavy datasets. • • ARIMA/SARIMAX – classical models for financial and operational forecasting. • • Neural Networks using PyTorch or TensorFlow. • • Hugging Face transformer models including BERT, RoBERTa, and others. • • AutoML frameworks such as Azure AutoML or H2O Driverless AI. • • Embedding models including text-embedding-3-large, Instructor-XL, and E5-large. Cloud, DevOps & Deployment (Azure): • • Azure Container Apps. • • Docker with multi-stage builds. • • Kubernetes / AKS. • • Redis Cache. • • Cosmos DB. • • GitHub Actions / Azure DevOps CI/CD. • • Application Insights and Azure Monitor for observability.

Requirements

• Agentic AI frameworks
• RAG expertise
• LLM API integration
• Machine Learning models
• Cloud, DevOps & Deployment

Preferred Technologies

• Agentic AI frameworks
• RAG expertise
• LLM API integration
• Machine Learning models
• Cloud, DevOps & Deployment