AI Architect

• Lead the design of end-to-end AI solution architectures aligned with business and technical requirements
• Design Generative AI and multi-agent systems, including orchestration, context management, retrieval strategies, tool integration, and human-in-the-loop workflows
• Architect solutions combining traditional machine learning, LLMs, business rules, and enterprise software components
• Define integration architectures, API contracts, data flows, versioning strategies, and reliability mechanisms
• Document key architectural decisions while balancing performance, scalability, security, reliability, accuracy, and cost
• Design production-ready AI systems with evaluation, monitoring, observability, security, and maintenance capabilities
• Build prototypes and proof-of-concepts to validate technical assumptions and accelerate delivery
• Review technical designs and support engineering teams with complex implementation challenges
• Collaborate with client stakeholders, enterprise architects, and engineering teams
• Lead technical discovery workshops, feasibility assessments, architecture proposals, project estimation, and implementation planning
• Evaluate emerging AI technologies and contribute reusable architectural patterns, standards, and best practices across Spyrosoft’s AI practice

• Proven experience designing and delivering complex AI solutions from concept to production
• Strong background in enterprise solution architecture, distributed systems, integration patterns, and scalable software design
• Practical experience with Generative AI, agentic systems, orchestration frameworks, tool calling, retrieval, context management, and human-in-the-loop solutions
• Solid understanding of machine learning architectures, model training and inference pipelines, model serving, and data dependencies
• Experience integrating AI solutions with enterprise applications, APIs, databases, and external services
• Ability to evaluate technical trade-offs across performance, scalability, security, reliability, maintainability, and cost
• Knowledge of production-grade AI engineering practices, including testing, evaluation, monitoring, observability, CI/CD, and lifecycle management
• Understanding of AI security, data privacy, access control, threat modeling, and risks related to LLM-powered systems
• Familiarity with MCP, agent-to-agent communication protocols, and emerging interoperability standards
• Hands-on engineering mindset with the ability to create prototypes and validate architectural assumptions
• Experience working with at least one major cloud platform
• Strong stakeholder management and technical leadership skills
• Excellent problem-solving and communication abilities
• Fluent English (spoken and written)
• Nice to have: experience with intelligent document processing and workflow automation solutions
• Nice to have: experience building knowledge-intensive applications
• Nice to have: knowledge of AI SDLC, MLOps/LLMOps practices, AI governance, and evaluation frameworks
• Nice to have: previous experience in consulting, software services, or technical pre-sales activities
• Nice to have: experience leading architecture workshops and multidisciplinary teams