AI Architect

Tech Stack:

• Generative AI (LLMs, RAG, Agentic AI, Multi-Agent Systems)

• MCP (Model Context Protocol)

• AI Orchestration Frameworks

• Machine Learning & MLOps/LLMOps

• REST APIs & Enterprise Integrations

• Distributed Systems Architecture

• Cloud AI Services (Azure, AWS, or Google Cloud)

• CI/CD, Monitoring & Observability Tools

• Databases, Vector Databases & Knowledge Retrieval Systems

• Security, Identity & Access Management

Project Description:

At Spyrosoft, we are growing our AI practice and looking for an experienced AI Architect to design and deliver advanced AI solutions for enterprise clients across multiple industries. In this role, you will shape end-to-end AI architectures that combine machine learning, Generative AI, agentic systems, and enterprise technologies.

You will be responsible for the complete AI solution lifecycle, from discovery and architecture design through implementation, deployment, and continuous improvement. Working closely with clients, architects, engineers, and business stakeholders, you will help define innovative and scalable AI strategies while providing hands-on technical leadership throughout the delivery process.

Required Qualifications:

• 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.

• Experience building knowledge-intensive applications.

• Knowledge of AI SDLC, MLOps/LLMOps practices, AI governance, and evaluation frameworks.

• Previous experience in consulting, software services, or technical pre-sales activities.

• Experience leading architecture workshops and multidisciplinary teams.

Scope of Services:

• 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 that combine traditional machine learning, LLMs, business rules, and enterprise software components.

• Define integration architectures, API contracts, data flows, versioning strategies, and reliability mechanisms.

• Make and document key architectural decisions, balancing performance, scalability, security, reliability, accuracy, and cost.

• Design production-ready AI systems with strong 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 in solving complex implementation challenges.

• Collaborate with client stakeholders, enterprise architects, and engineering teams to align solutions with existing technology ecosystems.

• Lead technical discovery workshops, feasibility assessments, architecture proposals, project estimation, and implementation planning.

• Evaluate emerging AI technologies and contribute to reusable architectural patterns, standards, and best practices across Spyrosoft’s AI practice.