AI Developer - Agentic AI & LLM

Job Overview : The ideal candidate will be responsible for designing, developing, and deploying intelligent AI agents, conversational AI solutions, and enterprise-grade AI applications leveraging modern AI frameworks and cloud technologies. Key Responsibilities : - Design, develop, and deploy AI-powered applications using Large Language Models (LLMs). - Build and manage autonomous AI agents capable of reasoning, planning, tool usage, and task execution. - Develop Agentic AI solutions using modern orchestration frameworks. - Implement Retrieval-Augmented Generation (RAG) architectures for enterprise knowledge management and search. - Integrate AI models with business applications, APIs, databases, and enterprise systems. - Fine-tune, optimize, and evaluate LLM performance for domain-specific use cases. - Design prompt engineering strategies to improve model accuracy and reliability. - Develop multi-agent systems and workflow automation solutions. - Collaborate with business stakeholders to identify AI opportunities and translate them into technical solutions. - Monitor AI system performance, security, governance, and compliance requirements. - Participate in architecture discussions, code reviews, and AI solution design. Required Technical Skills : - Programming Languages : Python (Mandatory), C# / .NET (Preferred), JavaScript / TypeScript, SQL - AI & Machine Learning : Large Language Models (LLMs), Generative AI, Prompt Engineering, Fine-Tuning Techniques, Model Evaluation, AI Safety & Responsible AI, Agentic AI - Frameworks & Platforms : LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, OpenAI APIs, Azure OpenAI Services - Cloud Platforms : Microsoft Azure, Azure Functions, AWS Bedrock (Preferred) - DevOps & Deployment : Git, Azure DevOps, Docker, Kubernetes (Preferred) Required Qualifications : - Minimum 2 years of hands-on experience with Generative AI and LLM-based application development. Strong understanding of AI architecture patterns, RAG, vector databases, and agent orchestration frameworks. Job Overview : The ideal candidate will be responsible for designing, developing, and deploying intelligent AI agents, conversational AI solutions, and enterprise-grade AI applications leveraging modern AI frameworks and cloud technologies. Key Responsibilities : - Design, develop, and deploy AI-powered applications using Large Language Models (LLMs). - Build and manage autonomous AI agents capable of reasoning, planning, tool usage, and task execution. - Develop Agentic AI solutions using modern orchestration frameworks. - Implement Retrieval-Augmented Generation (RAG) architectures for enterprise knowledge management and search. - Integrate AI models with business applications, APIs, databases, and enterprise systems. - Fine-tune, optimize, and evaluate LLM performance for domain-specific use cases. - Design prompt engineering strategies to improve model accuracy and reliability. - Develop multi-agent systems and workflow automation solutions. - Collaborate with business stakeholders to identify AI opportunities and translate them into technical solutions. - Monitor AI system performance, security, governance, and compliance requirements. - Participate in architecture discussions, code reviews, and AI solution design. Required Technical Skills : - Programming Languages : Python (Mandatory), C# / .NET (Preferred), JavaScript / TypeScript, SQL - AI & Machine Learning : Large Language Models (LLMs), Generative AI, Prompt Engineering, Fine-Tuning Techniques, Model Evaluation, AI Safety & Responsible AI, Agentic AI - Frameworks & Platforms : LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, OpenAI APIs, Azure OpenAI Services - Cloud Platforms : Microsoft Azure, Azure Functions, AWS Bedrock (Preferred) - DevOps & Deployment : Git, Azure DevOps, Docker, Kubernetes (Preferred) Required Qualifications : - Minimum 2 years of hands-on experience with Generative AI and LLM-based application development. Strong understanding of AI architecture patterns, RAG, vector databases, and agent orchestration frameworks.