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 ideal candidate will have deep experience with LLMs, multi-agent systems, orchestration frameworks, AI infrastructure, and enterprise AI architecture. The role will involve designing end-to-end AI solutions, guiding engineering teams, and enabling reliable deployment of agent-based AI systems across enterprise environments.
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 business 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 AI 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 engineering teams in deploying scalable AI solutions across cloud platforms such as Azure, AWS, and GCP.
• Evaluate and adopt emerging technologies in autonomous AI, reasoning systems, and agent frameworks.
• Provide technical leadership and mentorship to AI/ML engineers.
• Define scalable AI architecture patterns aligned with enterprise security, reliability, and performance requirements.
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.
• Strong proficiency in 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.
• Strong understanding of RAG architectures, prompt engineering, tool integration, and memory management for 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.
• Experience designing enterprise-grade AI solutions with appropriate governance, monitoring, security, and scalability considerations.
