AI Solution Architect

Position : AI Solution Architect
Location : Bellevue, WA/ Overland Park, KS/ Herndon, VA
Duration : Fulltime

Job Description:
Roles & Responsibilities:
• The AI Solution Architect will lead the design and delivery of secure, scalable AI solutions spanning Generative AI, agentic AI, machine learning, data, and cloud.
• The role will architect governed AI agents, built on robust agent loops and harnesses with guardrails, human oversight, and observability, that execute multi-step business tasks reliably.
• Working with business, engineering, and governance teams, the architect will take AI use cases from concept to production.

Key Responsibilities:
• Define end-to-end AI solution architectures aligned with business outcomes, enterprise standards, and technology strategy.
• Assess business use cases and recommend suitable approaches using Generative AI, machine learning, natural language processing, computer vision, predictive analytics, or intelligent automation.
• Design architectures for large language model applications, including prompt orchestration, retrieval-augmented generation, agents, vector databases, model gateways, and enterprise integrations.
• Design agentic AI solutions built on the reason–act–observe agent loop, with defined step limits, exit conditions, and escalation paths for reliable multi-step task execution.
• Architect agent harnesses covering context engineering, tool orchestration (including Model Context Protocol), state persistence, checkpointing, and output verification.
• Embed human-in-the-loop approvals, sandboxed tool execution, and cost, retry, and resource guardrails into agentic workflows.
• Establish agent evaluation, tracing, and monitoring practices to measure task success, reliability, cost, and failure patterns in production.
• Evaluate and select foundation models, AI platforms, cloud services, frameworks, and tools based on performance, security, scalability, cost, and compliance requirements.
• Establish reusable reference architectures, design patterns, accelerators, and guardrails for responsible AI adoption.
• Design data ingestion, preparation, feature, embedding, retrieval, model-serving, API, and application-integration layers.
• Guide engineering teams on model development, fine-tuning, evaluation, deployment, monitoring, and lifecycle management.
• Define MLOps and LLMOps practices covering versioning, automated testing, deployment pipelines, observability, drift detection, rollback, and continuous improvement.
• Embed security, privacy, explainability, fairness, human oversight, intellectual-property protection, and regulatory controls into solution designs.
• Lead architecture reviews, technical discovery workshops, proof-of-concept initiatives, and production-readiness assessments.
• Create architecture diagrams, solution blueprints, technical proposals, effort estimates, implementation roadmaps, and risk-mitigation plans.
• Engage with customers and senior stakehold ers to communicate solution value, design choices, dependencies, risks, and trade-offs.
• Mentor technical teams and contribute to AI capability development, knowledge assets, and engineering standards.

Technical Competencies:
• Generative AI: Large language models, prompt engineering, retrieval-augmented generation, agents, model evaluation, fine-tuning, guardrails, and vector search.
• Agentic AI: Agent loop design (ReAct and planner–executor patterns), agent harness engineering, multi-agent orchestration, tool calling and Model Context Protocol, memory and state management, human-in-the-loop controls, and agent evaluation and observability (e.g., OpenTelemetry tracing).
• AI/ML: Supervised and unsupervised learning, deep learning, natural language processing, computer vision, forecasting, feature engineering, and model validation.
• Platforms and Frameworks: Azure AI Foundry/Azure Machine Learning, AWS Bedrock/SageMaker, Google Vertex AI, OpenAI-compatible services, Hugging Face, LangChain, LlamaIndex, or similar technologies.
• Data and Integration: SQL and NoSQL databases, data lakes or lakehouses, vector databases, APIs, messaging, ETL/ELT, metadata, and data governance.
• APIs and Microservices: RESTful, GraphQL, OpenAPI specifications, API gateways and management, microservices and event-driven architecture, service mesh, asynchronous messaging (e.g., Kafka), API security (OAuth 2.0, OIDC, JWT), and exposing AI models and agents as reusable, scalable services.
• Engineering and Operations: Python, containerization, Kubernetes, CI/CD, MLOps, LLMOps, model monitoring, observability, and infrastructure as code.
• Architecture and Governance: Cloud-native design, security architecture, identity and access management, scalability, resiliency, FinOps, responsible AI, and regulatory compliance.

Preferred Qualifications:
• Experience in consulting, presales, solution estimation, proposal development, or customer-facing architecture roles.
• Experience delivering AI solutions in regulated or large-scale enterprise environments.
• Knowledge of industry-specific AI use cases, particularly in telecommunications, customer operations, quality engineering, network operations, or business observability.
• Experience establishing AI centers of excellence, reusable AI platforms, or enterprise AI governance frameworks.