Role description
Experience 11 to 15 years
Location Atlanta Georgia
Role Summary
Lead the design and delivery of AIML and GenAI solutions across broker operationsincluding placement quoting underwriting support claims advocacy and client servicing
Drive AI integration across Azure Databricks Python ML and legacy NET systems to modernise broker workflows and enhance decisionmaking productivity and client experience
Key Responsibilities
Engineer autonomous secure agents
Set up agent evaluation automation
Architect endtoend AIML 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
Primary Skills MustHave
AIML GenAI
Strong 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
oContext management MCP elicitation notification patterns MCPA2A protocols CodeAct Code Interpreter Agent Skill evaluationmanagement Agent harness and RAG
Agent evaluation expertise including automation of evaluation workflows
Engineering Agent Skill working alongside Insurance SMEs
Python Agent Engineering
Advanced Python solutioning for production AI and agent systems
Proficient in agentic frameworks and productiongrade agent design including multiagent patterns
Humanintheloop HITL workflow and interaction design for agent systems
Ability to leverage coding agents and specdriven development across all SDLC phases
Experience with containers for scalable portable deployment of AI and agent workloads
Azure Data Platform
Azure solutions architecture across AI data integration security CICD and observability
Handson with enterprise Azure services for AIdata platforms and secure application integration
Security engineering for agents and platforms including OAuth2 Azure permissions IAM policies and finegrained 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 costefficient architecture for enterprise AI solutions
Integration APIs
Strong integration skills with REST APIs webhooks and APIled eventdriven integration with enterprise systems
Expertise with relational NoSQL and graph databases
Secondary Skill
Databricks Lakehouse Preferred
Experience with Delta Lake ETLELT feature engineering and job orchestration
Experience creating finetuning datasets for domainspecific AI use cases
Experience with domainspecialized model finetuning 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
AI integration in legacyNET broker platforms
MLOps model governance and drift monitoring
Security compliance PII handling auditability
Realtimeeventdriven architectures for trading workflows
Analytics dashboards for broker performance and AI impact
Outcome Focus
Faster placement cycles and improved quote quality
Increased broker productivity and automation STP
Better client insights and retention
