Job Summary
We are seeking a Junior AI Engineer to design, build, and deploy enterprise-grade Generative AI applications leveraging cloud-native architectures and modern LLM ecosystems. The ideal candidate will have strong data engineering foundations combined with expertise in Retrieval-Augmented Generation (RAG), agentic workflows, and AI service integration.
Responsibilities
- Design and develop GenAI applications using Python and enterprise LLM platforms.
- Build scalable data pipelines for structured and unstructured data processing.
- Implement Retrieval-Augmented Generation (RAG) systems and vector-based search solutions.
- Integrate LLMs with enterprise applications through APIs and workflows.
- Develop cloud-native AI services on AWS, Azure, GCP, Databricks (DBRX), or Snowflake.
- Create agent-based solutions using LangChain and LangGraph.
- Ensure data quality, governance, and security compliance across AI workflows.
- Optimize performance, scalability, and reliability of AI solutions.
Technical Requirements
- Python
- SQL
- ETL/ELT
- Data Modeling
- Data Quality
- Cloud-native Development
- API Integration
- Data Engineering
- ETL
- Document Processing
- Workflow design
- knowledge of LLM development
- RAG
- Prompt Engineering
- AI Coding Assistants
Advanced Skills
- RAG Data Pipelines
- LLM Data Integration
- AI Service Integration
- Scalable AI Deployments
- Agent Orchestration
- Tool Calling
- LangGraph
- LangChain
Additional Responsibility
- 1+ years of experience with Hugging Face, LangChain, OpenAI, Anthropic, Gemini, or open-source LLMs.
- Familiarity with knowledge graphs and semantic search.
- Experience building production-grade GenAI solutions
Educational Requirement
Preferred Skills
- Technology->AI-Data science->Machine Learning, Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag)
Service Line
