AI Architect

Overview The AI Solutions Architect defines the technical direction for AI, ML, and data‑driven capabilities across the enterprise. The role shapes the architecture for intelligent, cloud‑native solutions leveraging machine learning, LLMs, automation, and modern data platforms while ensuring security, scalability, compliance, and operational resilience. Key Responsibilities Architect end‑to‑end AI/ML and LLM solutions spanning data pipelines, training workflows, inference layers, and governance. Translate business priorities into AI‑enabled solution designs and reusable reference architectures. Maintain and evolve architecture standards; champion adoption across delivery teams. Mentor engineering teams and contribute to architecture communities of practice. Qualifications Bachelor’s degree in Computer Science or related field. 15+ years in software architecture with strong experience in distributed systems. 5+ years designing or implementing AI/ML or LLM‑based solutions. 5+ years hands‑on development experience (Java/Kotlin/C#/Python). Expertise in cloud‑native architecture, APIs, microservices, and event‑driven platforms. Strong understanding of AI security, model governance, and data privacy controls. Experience with data pipelines, databases, and high‑scale systems. Preferred Experience Master’s degree or equivalent experience. Experience with GCP (Vertex AI, BigQuery) and Kubernetes. Familiarity with orchestration frameworks, vector databases, embeddings, and model gateways. Required Skills Design end‑to‑end architectures for AI/ML and LLM‑based solutions, spanning data pipelines, training workflows, inference layers, and governance. Evaluate emerging AI and cloud capabilities and guide technology adoption aligned with enterprise strategy. Engage early with the Governance organization to align on standards, secure required approvals, and deliver the architecture artifacts needed to support enterprise‑grade solution decisions. #J-18808-Ljbffr Saransh Inc