AI Solutions Architect

Flashii

United States

United States

Über

Location: Onsite – San Francisco, CA Employment Type: Full-Time About the Role: We are seeking an experienced AI Solutions Architect to lead the design, architecture, and delivery of an enterprise-scale Agentic AI platform. This individual will drive the technical vision for multi-agent AI systems, Retrieval-Augmented Generation (RAG), MCP-based tool integrations, and scalable microservices architecture that enables enterprises to compose, govern, and operate domain-specific AI agents at scale. The ideal candidate will have deep expertise in enterprise AI architecture, distributed systems, cloud-native engineering, and production-grade LLM platforms. This role requires both hands-on technical leadership and the ability to collaborate closely with engineering, product, presales, and enterprise stakeholders. Responsibilities: Lead the end-to-end architecture and implementation of multi-agent AI systems using frameworks such as LangChain, LangGraph, and Model Context Protocol (MCP) Design planner-executor patterns, sub-agent hierarchies, tool orchestration, retry logic, memory/context handling, and token optimization strategies Develop scalable agentic workflows supporting enterprise automation and reusable AI capabilities Architect and optimize enterprise-grade RAG pipelines using vector databases such as pgvector and Qdrant Implement hybrid retrieval, semantic search, re-ranking, and intelligent chunking strategies Ground AI agents using structured and unstructured enterprise knowledge sources Design scalable reference architectures and AI solution blueprints for enterprise customers Translate complex business requirements into scalable AI roadmaps and reusable platform accelerators Support regulated and consumer-facing enterprise environments Build event-driven microservices architectures leveraging Kafka, PostgreSQL, vector databases, and Kubernetes Design cloud-native deployment topologies optimized for high-throughput AI inference workloads Develop APIs and backend services using FastAPI, Node.js, Spring Boot, or similar frameworks Establish AI engineering best practices across training, fine-tuning, prompt management, evaluation pipelines, drift detection, and rollback strategies Build structured evaluation frameworks and observability tooling for production AI systems Ensure scalability, reliability, and governance across the Agentic Development Lifecycle (ADLC) Implement observability and monitoring solutions using Prometheus, Grafana, OpenTelemetry, and distributed tracing frameworks Track agent reasoning, tool calls, token consumption, and quality metrics Enable auditability, compliance, and human-in-the-loop governance Create reusable AI engineering patterns, skills, sub-agents, evaluation harnesses, and platform components Standardize reusable development frameworks across multiple product and business lines Drive adoption of AI-assisted development tooling including Claude Code, Playwright MCP, Cursor, and related agentic engineering tools Support AI-enhanced planning, code generation, testing, and release validation workflows Partner with sales, presales, and customer success teams on enterprise AI initiatives Lead technical discovery sessions, workshops, architecture reviews, and AI roadmap discussions Support solution positioning and enterprise AI transformation initiatives Qualifications: 8+ years of software engineering and solution architecture experience 3+ years of hands-on experience designing and deploying LLM-based or Agentic AI systems in production environments Deep expertise with, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), MCP / AI tool orchestration, Prompt engineering, Context engineering, Token optimization Strong programming experience in Python and TypeScript (Java preferred) Experience building scalable microservices using FastAPI, Spring Boot, Node.js, or related frameworks Hands-on experience with, AWS, Azure, or GCP, Kubernetes, Docker, Terraform, CI/CD pipelines Strong understanding of, MLOps, AI model lifecycle management, Evaluation framework, Drift detection, AI observability Proven enterprise solution architecture experience translating business requirements into scalable AI solutions

United States

Sprachkenntnisse

English

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