AI Architect

Job Title : AI Architect Location : Atlanta, GA (Onsite – Hybrid)Job Description : Role SummaryLead the design and delivery of AI/ML and GenAI solutions across broker operations - Require hands-on high-code agentic development experience using langgraph, langchain or similar toolsDrive AI integration across Azure, Databricks, Python ML, and legacy .NET systems to modernise broker workflows and enhance decision-making, productivity, and client experience.Key ResponsibilitiesEngineer autonomous, secure agentsSet up agent evaluation automation.Architect end-to-end AI/ML & GenAI solutions from design to production.Translate broker use cases: submission triage, quote comparison, document ingestion (IDP), recommendation engines, agent assist, client insights.Define enterprise AI patterns, standards, and reusable components.Ensure scalability, performance, explainability, compliance, and cost efficiency.Lead technical governance, design reviews, and stakeholder engagement.Design data & ML platforms on Azure + Databricks Embed AI into broker platforms (placement, quoting, CRM, document systems, .NET apps).Understand the insurance domain and design and implement extensible, evolvable schemas.Core Skills (Must-Have)AI/ML & GenAIStrong ML lifecycle expertise and GenAI design (RAG, prompting, retrieval, evaluation).Ability to choose optimal approach (ML vs GenAI) based on business need.GenAI agentic engineering experience with agentic frameworks.Context management, MCP elicitation, notification patterns, MCP/A2A protocols, CodeAct Code Interpreter, Agent Skill evaluation/management, Agent harness, and RAG.Agent evaluation expertise, including automation of evaluation workflows.Engineering Agent Skill working alongside Insurance SMEs.Python & Agent EngineeringAdvanced Python solutioning for production AI and agent systems.Proficient in agentic frameworks and production-grade agent design, including multi-agent patterns.Human-in-the-loop (HITL) workflow and interaction design for agent systems.Ability to leverage coding agents and spec-driven development across all SDLC phases.Experience with containers for scalable, portable deployment of AI and agent workloads.Azure & Data PlatformAzure solutions architecture across AI, data, integration, security, CI/CD, and observability.Hands-on with enterprise Azure services for AI/data platforms and secure application integration.Security engineering for agents and platforms, including OAuth2, Azure permissions, IAM, policies, and fine-grained access control (FGAC).Experience with credentials and secrets management for enterprise AI systems.Familiarity with infrastructure as Code using Terraform for Azure environment provisioning and platform standardization.Ability to implement scalable, resilient, and cost-efficient architecture for enterprise AI solutions.Integration & APIsStrong integration skills with REST APIs, webhooks, and API-led, event-driven integration with enterprise systems.Expertise with relational, NoSQL, and graph databases.Databricks & Lakehouse (Preferred)Experience with Delta Lake, ETL/ELT, feature engineering, and job orchestration.Experience creating fine-tuning datasets for domain-specific AI use cases.Experience with domain-specialized model fine-tuning for insurance and broker workflows.Broker Domain Knowledge (Preferred)Understanding of wholesale insurance and broker workflows: submissions, placement, quoting, renewals, and client servicing.Ability to map AI to outcomes such as placement speed, hit ratio, productivity, and client retention.Understand the insurance domain and design and implement extensible, evolvable schemas and ontologies.Differentiators (Preferred)AI integration in legacy/.NET broker platformsMLOps, model governance, and drift monitoringSecurity & compliance (PII handling, auditability)Real-time/event-driven architectures for trading workflowsAnalytics dashboards for broker performance and AI impact