AI Development Engineer

Job Responsibilities:          

@1. Leverage AI tools and frameworks not only for automation but as creative partners—experimenting with prompts, refining outputs, and integrating generative models into the development workflow.         

@2. Demonstrate critical evaluation of AI-generated results, ensuring quality, maintainability, and reliability.          

@3. Lead the design, development, and deployment of generative AI systems, including LLM-based agents and RAG pipelines.         @4. Collaborate with cross-functional teams (PMs, data scientists, backend engineers) to define product scenarios and optimize AI workflows.                                             

@5. Architect scalable AI solutions using modern cloud platforms (Azure) and MLOps tools (MLFlow, Kubeflow, CI/CD).     

@6. Implement and optimize vector databases (e.g., FAISS, Pinecone, Qdrant) and retrieval systems for enterprise knowledge integration.          

@7. Drive prompt engineering, model fine-tuning, and performance evaluation strategies.              

@8. Contribute to the team’s technical roadmap, mentor junior engineers, and promote best practices in AI development.        

Job Requirements:  
1. Proven experience with generative AI, LLMs (OpenAI), and RAG architectures.  
2. Strong programming skills in Python and familiarity with frameworks like LangChain, LangGraph, FastAPI.  
3. Experience with cloud-native AI development (Azure AI Studio, Cognitive Search, AKS, Azure ML).  
4. Solid understanding of backend systems, microservices, and distributed computing.  
5. Familiarity with MLOps practices and tools for scalable deployment and monitoring.  
6. Excellent communication skills and a proactive mindset to contribute ideas beyond assigned tasks.  
7. Preferred Experience in building production-grade AI agents for enterprise use cases. Background in NLP, transformers, and model orchestration.  
8. Prior contributions to open-source AI projects or publications. 

Project Background:  @he Speech Project was launched in 2023, establishing a strong and trustworthy partnership with the client. The project primarily focuses on cloud-based platforms and big data computing, involving: the training and iterative optimization of acoustic and language models for speech recognition, the development of automated tools and systems related to speech recognition model building, and experimental research on cutting-edge technologies in speech recognition.