As a GenAI Developer, your role will involve designing, building, and deploying scalable generative AI solutions for enterprise use cases. You should have strong hands-on experience in building production-grade AI/ML systems, with expertise in LLMs, RAG architectures, and AI orchestration frameworks. Key Responsibilities:
Designing and developing Generative AI applications using LLMs and advanced AI frameworks
Building and optimizing Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models
Developing and implementing Agentic AI systems, including multi-agent workflows and supervisor agents
Integrating AI solutions with cloud platforms such as Azure or AWS
Working with orchestration tools like LangChain, LangGraph, LlamaIndex, Airflow, or Prefect
Fine-tuning LLMs and implementing prompt engineering best practices
Collaborating with cross-functional teams to deliver scalable AI solutions
Ensuring production readiness, monitoring, and optimization of AI models
Qualifications Required:
5+ years of experience building production-level AI/ML solutions using Python
Experience in Agentic AI systems, including agent-to-agent communication, MCP, supervisor agents, micro-agents
Advanced experience with Azure AI (Azure OpenAI, Cognitive Services, Azure ML) OR AWS AI (Bedrock, SageMaker, LangChain integration)
Strong experience with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Airflow, Prefect, etc. - Deep understanding of Large Language Models (LLMs), fine-tuning, and prompt engineering
Good-to-Have Skills:
Experience with SQL and DevOps practices
Prior experience leading or mentoring development teams
Exposure to CI/CD pipelines and scalable deployments
Please note that the job description did not provide any additional details about the company. As a GenAI Developer, your role will involve designing, building, and deploying scalable generative AI solutions for enterprise use cases. You should have strong hands-on experience in building production-grade AI/ML systems, with expertise in LLMs, RAG architectures, and AI orchestration frameworks. Key Responsibilities:
Designing and developing Generative AI applications using LLMs and advanced AI frameworks
Building and optimizing Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models
Developing and implementing Agentic AI systems, including multi-agent workflows and supervisor agents
Integrating AI solutions with cloud platforms such as Azure or AWS
Working with orchestration tools like LangChain, LangGraph, LlamaIndex, Airflow, or Prefect
Fine-tuning LLMs and implementing prompt engineering best practices
Collaborating with cross-functional teams to deliver scalable AI solutions
Ensuring production readiness, monitoring, and optimization of AI models
Qualifications Required:
5+ years of experience building production-level AI/ML solutions using Python
Experience in Agentic AI systems, including agent-to-agent communication, MCP, supervisor agents, micro-agents
Advanced experience with Azure AI (Azure OpenAI, Cognitive Services, Azure ML) OR AWS AI (Bedrock, SageMaker, LangChain integration)
Strong experience with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Airflow, Prefect, etc. - Deep understanding of Large Language Models (LLMs), fine-tuning, and prompt engineering
Good-to-Have Skills:
Experience with SQL and DevOps practices
Prior experience leading or mentoring development teams
Exposure to CI/CD pipelines and scalable deployments
Please note that the job description did not provide any additional details about the company.