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Company Overview
A leading global technology, defense, and engineering group with offices across Asia, Europe, the Middle East, and the U.S., serving customers in over 100 countries. The organization leverages innovation and technology to solve real-world problems and improve lives through its aerospace, smart city, defense, and public security businesses. The company provides cybersecurity solutions, managed security services, and builds security operations centers to strengthen enterprise and government cyber resilience.
Summary
The Senior AI Systems Architect will design, develop, and operationalize agentic AI systems that integrate seamlessly with enterprise IT and Service-Oriented Architecture (SOA) frameworks. The role requires bridging technical depth with enterprise-scale impact to drive the adoption of agentic AI within secure, scalable environments.
Responsibilities
Design and operationalize agentic AI systems within enterprise IT and SOA frameworks
Define system components, APIs, and interfaces for agent-based intelligence in scalable platforms
Ensure adaptability, reusability, and maintainability of AI services across enterprise applications
Collaborate with business and IT stakeholders to align AI capabilities with strategic goals
Translate business requirements into intelligent agent-driven processes to optimize automation and decision-making
Conduct feasibility assessments and define KPIs for agent-enabled systems
Architect and manage infrastructure supporting AI workflows, including real-time data pipelines, feature stores, and model registries
Oversee the full lifecycle of AI agents, from model training and orchestration to deployment, monitoring, and iteration
Integrate ML models, reasoning engines, and multi-agent systems into core IT and product platforms
Design systems for distributed, high-performance, real-time AI processing
Optimize AI infrastructure for low-latency decision loops and efficient agent coordination
Partner with data engineers, software developers, MLOps, and DevOps teams to ensure robust lifecycle management of AI agents
Define and enforce best practices for agent orchestration, CI/CD, and live system monitoring
Provide strategic and technical leadership for AI agent deployment across the enterprise
Requirements
8+ years of experience in AI systems architecture, data science, or enterprise software engineering
Proven experience implementing AI/ML systems in SOA or cloud-native architectures
Hands-on experience integrating autonomous AI agents or models into enterprise products
Proficiency with AI/ML frameworks such as PyTorch, TensorFlow, and Scikit-learn
Advanced Python development skills; experience with containerization and orchestration (Docker, Kubernetes)
Deep knowledge of data pipelines, event-driven architectures (Kafka, Flink), and real-time AI processing
Experience with MLOps, model versioning, and observability tools in production
Strong analytical, strategic thinking, and problem-solving skills
Excellent communication skills across technical and non-technical stakeholders
Preferred Qualifications
Certifications in AI, Machine Learning, or Agent-Based Systems
Experience with Large Language Models (LLMs), multi-agent orchestration frameworks, and cognitive architectures
Disclaimer
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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
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