AI Engineer (LLM)

Key Responsibilities

• Design, develop, and deploy LLM-powered applications and AI agents.


• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases.


• Develop prompt engineering strategies and optimize model performance.


• Integrate LLM APIs (OpenAI, Anthropic, Google Gemini, Azure OpenAI, etc.) into applications.


• Fine-tune and evaluate open-source LLMs where applicable.


• Develop AI workflows using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or similar.


• Build REST APIs and microservices for AI applications.


• Implement AI guardrails, content moderation, and security best practices.


• Optimize inference performance, latency, and operational costs.


• Collaborate with cross-functional teams to deliver production-ready AI solutions.


• Stay current with emerging AI technologies, research, and industry trends.

Required Skills

• Strong proficiency in Python.


• Experience with Large Language Models and Generative AI.


• Hands-on experience with LangChain, LlamaIndex, CrewAI, AutoGen, or similar frameworks.


• Knowledge of RAG architectures and vector databases (Pinecone, Weaviate, Chroma, FAISS, Milvus, etc.).


• Experience with embedding models and semantic search.


• Familiarity with OpenAI, Anthropic, Gemini, Azure OpenAI, or open-source LLMs (Llama, Mistral, Qwen, etc.).


• Experience with FastAPI, Flask, or Django.


• Strong understanding of REST APIs and cloud platforms (AWS, Azure, or GCP).


• Knowledge of Docker, Kubernetes, and CI/CD pipelines.


• Familiarity with Git and software development best practices.


• Understanding of AI evaluation metrics, prompt optimization, and model monitoring.