Solution Architect

AI Solution Architect Roles & Responsibilities Role Summary 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.

• 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. Education Bachelor’s degree, BACHELOR OF COMPUTER SCIENCE

• Discretionary Annual Incentive.
• Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
• Family Support: Maternal & Parental Leaves.
• Insurance Options: Auto & Home Insurance, Identity Theft Protection.
• Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
• Time Off: Vacation, Time Off, Sick Leave & Holidays.