AI Engineer (Gen AI, Gcp/ snowflake/aws)

AI Engineer (DBRX, AWS, Azure, GCP, Snowflake variants) Core Skills: 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 Semantic Layers, Data Governance, Memory Management, Autonomous Agents, Agent Routing, State Management, Memory Handling, Tool Integration

Responsibilities

Design, develop, and deploy Generative AI applications using LLMs and foundation models. Develop AI solutions utilizing RAG (Retrieval-Augmented Generation) architectures. Build and optimize AI pipelines using Python, LangChain, LlamaIndex, and vector databases. Integrate AI solutions with AWS, GCP, and Snowflake environments. Fine-tune and evaluate LLMs for domain-specific business use cases. Create scalable APIs and microservices for AI model deployment. Collaborate with data engineers, ML engineers, and business stakeholders. Implement prompt engineering techniques and model performance optimization. Ensure data security, governance, and compliance in AI applications. Monitor and improve AI model performance in production environments.

Requirements

Strong programming experience in Python. Hands-on experience with Generative AI, LLMs, Prompt Engineering, and RAG. Experience with frameworks such as: LangChain LlamaIndex Hugging Face OpenAI APIs Vertex AI / Bedrock Strong cloud experience in AWS or GCP. Hands-on experience with Snowflake including data integration and optimization. Experience with vector databases such as Pinecone, FAISS, ChromaDB, or Weaviate. Knowledge of REST APIs, microservices, and containerization (Docker/Kubernetes). Familiarity with MLOps and CI/CD practices.