Job Description:
The Generative AI Engineer is responsible for designing, developing, and deploying advanced AI solutions using Large Language Models (LLMs), agentic frameworks, and Retrieval‑Augmented Generation (RAG) architectures. The role involves building intelligent autonomous systems, optimizing model performance, integrating AI with enterprise systems, and deploying scalable solutions on cloud platforms.
Generative AI & LLM Development
-
Design and build Generative AI applications using Large Language Models (LLMs).
-
Develop agentic AI solutions, including autonomous agents, multi‑agent systems, and workflow‑driven decision engines.
-
Build AI applications using frameworks such as LangChain, LangGraph, or similar agent frameworks.
-
Implement prompt engineering, tool calling, memory management, and agent orchestration.
RAG & Vector Database Engineering
-
Implement RAG architectures using vector databases.
-
Work with embeddings, vector stores, and model evaluation techniques.
-
Apply context‑engineering strategies to reduce token usage and latency.
AI Integration & Enterprise Systems
-
Integrate AI services with enterprise APIs, middleware, and backend systems.
-
Evaluate and improve AI agent performance using defined metrics and evaluation frameworks.
Cloud Deployment
-
Deploy scalable AI solutions on Azure or AWS cloud platforms.
-
Utilize cloud‑native services for model hosting, orchestration, and monitoring.
Required Skills & Experience
-
Strong experience with LLMs, Generative AI, RAG, and agentic AI frameworks.
-
Proficiency in Python and machine learning workflows.
-
Hands‑on experience with LangChain, LangGraph, or similar frameworks.
-
Experience with vector databases (e.g., Pinecone, FAISS, Chroma).
-
Understanding of embeddings, model evaluation, and optimization techniques.
-
Experience deploying AI solutions on Azure or AWS.
