Senior Solutions Architect (AI & Agentic Systems)

• System Design: Design robust, scalable, and secure end-to-end architectures for complex distributed systems, ensuring high availability and fault tolerance.
• Integration Strategy: Define patterns for integrating AI capabilities into existing enterprise workflows, legacy systems, and microservices architectures.
• Technical Governance: Establish architectural standards, code quality guidelines across engineering teams.
• Stakeholder Management: Translate business requirements into technical specifications and communicate architectural decisions to C-level executives and product leaders.

• Agentic Frameworks: Architect Multi-Agent Systems where specialized agents (Planning, Execution, Research) collaborate to solve complex workflows.
• LLM Orchestration: Design the inference layer for managing multiple LLM providers (OpenAI, Anthropic, Gemini, local models), implementing fallback strategies, caching, and cost optimization.
• Memory Context: Architect advanced context management systems, including Vector Databases (RAG) and shared memory structures for long-running agent sessions.
• Tooling Ecosystem: Design secure protocols for agents to interface with external tools (File systems, Browsers, APIs) via standards like Model Context Protocol (MCP) .

• Experience: 10+ years in software engineering with 5+ years as a Solution Architect.
• Cloud Native: Expert knowledge of cloud platforms (AWS, Azure, or GCP), containerization (Kubernetes/Docker), and serverless patterns.
• Backend Engineering: Deep proficiency in TypeScript/Node.js or Python , with strong experience in API design (REST, GraphQL, WebSockets).
• Data Architecture: Experience designing complex data models using both Relational (PostgreSQL, SQLite) and NoSQL databases.

• Agentic AI: Proven understanding of agent architectures (ReAct, Plan-and-Solve), tool calling, and multi-agent orchestration patterns.
• AI Infrastructure: Experience building or architecting platforms that leverage LLMs , Embeddings , and Vector Stores (Pinecone, Weaviate, Milvus).
• MLOps: Familiarity with the lifecycle of deploying and monitoring AI models in production.

• Experience with Tauri or Electron for cross-platform desktop agent deployment.
• Hands-on experience with LangChain , AutoGPT , or custom agent frameworks.
• Knowledge of local-first architectures and embedded databases (SQLite).
• Certifications: AWS Certified Solutions Architect (Professional) or similar.

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