Job Summary
We are seeking an experienced AI Architect with strong expertise in Agentic AI systems to design and implement intelligent, autonomous AI solutions capable of reasoning, planning, and executing complex tasks. The role requires deep experience with large language models, multi-agent systems, orchestration frameworks, AI infrastructure, and scalable cloud deployments. The AI Architect will define end-to-end AI architectures, integrate AI agents with enterprise systems and data platforms, establish governance and observability practices, and provide technical leadership and mentorship to AI/ML engineering teams.
Key Responsibilities
• Design and architect Agentic AI systems capable of autonomous reasoning, decision-making, and task execution.
• Build and implement multi-agent frameworks that collaborate to solve complex workflows.
• Define architectures for LLM-powered applications, including prompt orchestration, tool usage, and memory management.
• Integrate AI agents with enterprise systems, APIs, and data platforms.
• Lead the design of AI pipelines, orchestration layers, and evaluation frameworks.
• Establish best practices for AI governance, safety, observability, and monitoring.
• Collaborate with product managers, data scientists, and engineering teams to translate business requirements into AI-driven solutions.
• Guide teams in deploying scalable AI solutions on Azure, AWS, or GCP.
• Evaluate and adopt emerging technologies in autonomous AI, reasoning systems, and agent frameworks.
• Provide technical leadership and mentorship to AI/ML engineers.
Required Qualifications
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 8+ years of experience in software engineering, machine learning, or AI architecture.
• 3+ years of experience working with LLM-based systems and generative AI solutions.
• Strong experience designing Agentic AI or multi-agent architectures.
• Proficiency with Python and AI/ML frameworks.
• Hands-on experience with LLM orchestration frameworks such as LangChain, AutoGen, CrewAI, Semantic Kernel, or similar.
• Experience working with vector databases such as Pinecone, Weaviate, or FAISS.
• Knowledge of RAG architectures, prompt engineering, tool integration, and memory management in AI agents.
• Experience deploying AI workloads in cloud environments such as Azure, AWS, or GCP.
• Strong understanding of API design, microservices architecture, and distributed systems.
Preferred Qualifications
• Experience evaluating emerging autonomous AI, reasoning, and agent framework technologies.
• Experience providing technical leadership and mentorship to AI/ML engineering teams.
