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AI Summary
Design, develop, and deploy LLM-powered applications and AI agents using RAG pipelines and vector databases. Collaborate with cross-functional teams to build scalable, production-ready AI solutions and optimize inference performance.
Job Summary
We are looking for an AI Engineer with hands-on experience in Large Language Models (LLMs), Generative AI, and modern AI frameworks. The ideal candidate will design, develop, and deploy intelligent AI applications powered by state-of-the-art language models. You will work closely with product, engineering, and data teams to build scalable AI solutions that solve real-world business problems.
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.
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