Jobs → Customer Support → AI Architect

About the Role We are seeking an experienced AI Architect to define and lead the design of scalable, secure, and production-ready artificial intelligence solutions. This role will partner with engineering, data, product, security, and business teams to translate strategic objectives into practical AI platforms and applications. Key Responsibilities • Develop enterprise AI architecture, technical roadmaps, and reusable design patterns. • Design solutions using generative AI, large language models, machine learning, NLP, and intelligent automation. • Architect retrieval-augmented generation systems, AI agents, model-serving infrastructure, and data pipelines. • Evaluate foundation models, cloud AI services, vector databases, orchestration frameworks, and MLOps tools. • Define standards for model governance, responsible AI, privacy, security, observability, and regulatory compliance. • Lead technical discovery, proofs of concept, architecture reviews, and production deployments. • Establish approaches for model evaluation, prompt management, monitoring, drift detection, and cost optimization. • Integrate AI capabilities with enterprise applications, APIs, data platforms, and cloud environments. • Mentor engineers and provide technical guidance across multiple AI initiatives. • Communicate architecture decisions, risks, tradeoffs, and recommendations to technical and executive stakeholders. Requirements • Bachelor's or master's degree in computer science, engineering, data science, or a related field. • 8+ years of experience in software, data, cloud, or solution architecture. • 3+ years of hands-on experience delivering machine-learning or AI solutions. • Strong knowledge of generative AI, LLMs, RAG, embeddings, vector search, prompt engineering, and AI agents. • Experience with Python and common AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, or LangChain. • Expertise in at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud. • Experience with APIs, microservices, distributed systems, data engineering, and container technologies. • Familiarity with MLOps and DevOps practices, including CI/CD, model registries, monitoring, and infrastructure as code. • Strong understanding of AI security, data privacy, governance, and responsible-AI principles. • Excellent communication, leadership, and stakeholder-management skills.