Python Ml_genai Engineers

Key Responsibilities

• Design and develop GenAI applications using Python and enterprise LLM platforms. 2. Build scalable data pipelines for structured and unstructured data processing. 3. Implement Retrieval-Augmented Generation (RAG) systems and vector-based search solutions. 4. Integrate LLMs with enterprise applications through APIs and workflows. 5. Develop cloud-native AI services on AWS, Azure, GCP, Databricks (DBRX), or Snowflake. 6. Create agent-based solutions using LangChain and LangGraph. 7. Ensure data quality, governance, and security compliance across AI workflows. 8. Optimize performance, scalability, and reliability of AI solutions.
• Additional

Key Responsibilities

1. 1+ years of

Experience

with Hugging Face, LangChain, OpenAI, Anthropic, Gemini, or open-source LLMs. 2. Familiarity with knowledge graphs and semantic search. 3.

Experience

building production-grade GenAI solutions

Technical and Professional

Requirements & Details

• 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
• Preferred Skills:
• Technology->AI-Data science->Machine Learning
• Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag)