AI Architect
- Quantum World Technologies Inc.
1 Days Ago
NA
Yearly
Atlanta-GA
5-7 Years
Required Skills: AI/GenAI Research, GenAI - LLMOps, MLOPS, Python, Deep Learning - AIOPS, Machine Learning - AIOPS, Python - Data Science
Job Description
Role Summary
- Lead 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 tools
- Drive AI integration across Azure, Databricks, Python ML, and legacy .NET systems to modernise broker workflows and enhance decision-making, productivity, and client experience.
Key Responsibilities
- Engineer autonomous, secure agents
- Set 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 & 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.
- 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 Engineering
- Advanced 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 Platform
- Azure 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 & APIs
- Strong 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 platforms
- MLOps, model governance, and drift monitoring
- Security & compliance (PII handling, auditability)
- Real-time/event-driven architectures for trading workflows
- Analytics dashboards for broker performance and AI impact
Skills
Mandatory Skills : AI/GenAI Research, GenAI - LLMOps, MLOPS, Python
Good to Have Skills : Deep Learning - AIOPS, Machine Learning - AIOPS, Python - Data Science
Good to Have Skills : Deep Learning - AIOPS, Machine Learning - AIOPS, Python - Data Science
