Role: Sr AI Developer / AI LEAD / Architect
Location: Atlanta, GA- 5days Onsite
Duration: Long term
ROLE OVERVIEW
We are seeking an experienced Lead Agentic AI Engineer to design, build, and scale agentic AI workflows for enterprise platforms, intelligent processes, and AI-driven client solutions. In this hands-on leadership role, you will architect multi-agent systems, integrate enterprise-grade LLM capabilities across Azure AI Foundry, OpenAI, and Anthropic Claude, and deliver production-ready AI solutions that meet the strict compliance and reliability standards of the insurance and financial services industry.
This is a highly hands-on technical leadership role where you will influence architecture, engineering practices, platform direction, and delivery execution.
KEY RESPONSIBILITIES
Agentic Workflow Design & Development
Architect and implement multi-agent systems using frameworks such as LangGraph, Semantic Kernel, AutoGen, or CrewAI
Design agent orchestration patterns including task decomposition, tool use, context management, memory, and human-in-the-loop (HITL) flows
Build reliable agentic pipelines handling document extraction, reasoning, routing, and structured output generation
Implement emerging agentic protocols including MCP (Model Context Protocol), Agent-to-Agent (A2A), AG-UI, and CodeAct Code Interpreter patterns
Design and evaluate agent skills, manage agent harnesses, and maintain agent capability registries
Design AI solutions capable of leveraging multiple LLM ecosystems including Azure OpenAI, OpenAI, Anthropic Claude, and open-source models based on workload characteristics, governance requirements, and cost/performance considerations
Full-Stack AI Application Engineering
Build full-stack AI-native applications using React with streaming agent interactions, AG-UI components, and HITL design patterns
Implement real-time agent communication interfaces with streaming output, MCP elicitation flows, and event-driven notifications
Design and expose REST APIs and webhook integrations for agent-to-system and system-to-system interactions
Azure AI Platform Engineering
Deploy and manage AI workloads on Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning, and AKS
Design event-driven and serverless architectures leveraging Azure Functions, Event Grid, Service Bus, and Azure API Management
Build scalable, resilient, cost-efficient cloud architectures aligned with Azure Solutions Architecture best practices
Implement Infrastructure as Code (IaC) using Terraform; establish pipeline-as-code and policy-as-code practices across CI/CD workflows
Containerize AI workloads using Docker and Kubernetes for portable, scalable deployment
LLM Integration & Enterprise Reliability
Lead prompt engineering, evaluation, and optimization strategies for OpenAI GPT models, Anthropic Claude, and Azure-hosted models
Implement RAG architectures using vector databases (Azure AI Search, PostgreSQL pgvector, Cosmos DB) and design extensible, evolvable schema and ontology models
Focus on making enterprise AI systems reliable, accurate, controllable, and production-ready - especially when working with LLMs like OpenAI GPT models or Anthropic Claude models
Design guardrails, output validation layers, and hallucination mitigation patterns for high-stakes enterprise workflows
Data Architecture - Relational, NoSQL & Graph
Design and work across relational databases (PostgreSQL, SQL Server), NoSQL stores (Cosmos DB, MongoDB), and graph databases for knowledge graph and ontology-driven AI use cases
Model extensible, evolvable schemas and domain ontologies that support AI reasoning, entity resolution, and semantic retrieval
Security & Identity
Implement enterprise-grade security across AI systems: OAuth 2.0, Azure IAM, role-based and fine-grained access control (FGAC), managed identities, and credentials management
Apply Azure security policies, RBAC, and least-privilege principles to AI platform components and agentic workflows
Ensure secure handling of credentials, API keys, and secrets using Azure Key Vault and secure secrets management practices
AI-Native Engineering Practices
Drive AI-assisted software engineering practices across the SDLC using copilots, autonomous coding agents, spec-driven development, and reusable engineering skills
Leverage coding agents effectively across all SDLC phases - from requirements and design through development, testing, and deployment
Help establish AI fluency standards and engineering productivity patterns across teams
Contribute to internal AI accelerators, engineering frameworks, and delivery automation capabilities
Enable engineering teams to effectively collaborate with AI systems while maintaining quality, governance, and reliability
Enterprise Governance & Responsible AI
Implement responsible AI controls including observability, auditability, security, prompt protection, PII handling, and human oversight mechanisms
Design enterprise-safe AI systems with governance, compliance, and reliability considerations built in from the ground up
Establish patterns for AI system transparency, explainability, and accountability in regulated industry contexts
Technical Leadership & Modern Delivery
Define AI engineering standards, design patterns, and best practices across the engineering organization
Lead architecture reviews, code reviews, and technical roadmap planning for AI platform capabilities
Mentor mid-level and junior engineers; foster a culture of AI-native engineering excellence
Operate effectively in fast-moving, iterative AI delivery environments where experimentation, rapid prototyping, and production hardening coexist
Balance innovation speed with engineering rigor, scalability, and maintainability
Communicate complex AI concepts clearly to both engineering and business stakeholders
Engage confidently with enterprise clients, architecture teams, and delivery leadership to shape AI solution direction
REQUIRED QUALIFICATIONS
Agentic AI & LLM Engineering
7+ years of software engineering experience with 3+ years in AI/ML or LLM-based systems
Hands-on experience building production-grade agentic or multi-agent AI workflows
Proficiency with GenAI agentic frameworks: LangGraph, Semantic Kernel, AutoGen, CrewAI, or LangChain
Working knowledge of agentic protocols: MCP (Model Context Protocol), A2A (Agent-to-Agent), AG-UI, and CodeAct/Code Interpreter patterns
Strong experience with context management strategies, agent skill design, agent evaluation, and agent harness construction
Proficiency with OpenAI APIs (GPT-4o, function calling, Assistants API) and Anthropic Claude APIs
RAG pipeline design: vector databases (Azure AI Search, PostgreSQL pgvector, Cosmos DB), chunking, embedding, and retrieval strategies
Ability to pivot across agentic framework approaches and managed agent platforms as the ecosystem evolves
Full-Stack & API Engineering
Full-stack experience with React; ability to build streaming agent interaction UIs, AG-UI components, and HITL design patterns
Strong REST API and webhook design and implementation skills
Proficiency in Python (intermediate level) and TypeScript for AI application and backend development
Cloud, Infrastructure & Architecture
Strong Azure platform experience: Azure AI Foundry, Azure OpenAI, Azure ML, AKS, Azure Functions, API Management, Event Grid, Service Bus
Infrastructure as Code using Terraform; pipeline-as-code and policy-as-code practices in CI/CD workflows
Proficiency with containers (Docker, Kubernetes) for scalable AI workload deployment
Ability to design and implement scalable, resilient, cost-efficient architectures on Azure
Event-driven architecture and serverless architecture design and implementation
Azure Solutions Architecture understanding across compute, networking, storage, security, and AI tiers
Data & Schema Design
Experience with relational databases (PostgreSQL, SQL Server), NoSQL (Cosmos DB, MongoDB), and graph databases
Ability to design extensible, evolvable schemas and domain ontologies that support AI reasoning and semantic retrieval
Security & Identity
OAuth 2.0 implementation and Azure IAM/RBAC: permissions, policies, managed identities, and fine-grained access control (FGAC)
Secure credentials management using Azure Key Vault and secrets management best practices
Security-first mindset for AI systems: prompt protection, PII handling, data boundary enforcement
Engineering Practices
Demonstrated ability to leverage coding agents and spec-driven development across all SDLC phases
Strong GitHub Copilot and AI-assisted development tooling proficiency
Experience leading technical teams and influencing engineering practices at an organizational level
NICE TO HAVE
Experience designing and deploying Engineering Agent Skills that work alongside domain SMEs in human-AI collaborative workflows
Wholesale insurance domain understanding: submission processing, broker/carrier workflows, market access, and underwriting operations
Hands-on experience transitioning from custom agentic frameworks to managed agent platforms (Azure AI Agent Service, OpenAI Assistants, etc.)
Experience with Databricks, MLflow, or Azure Databricks for data and model pipelines
Prior work on document intelligence platforms (OCR, extraction, classification, IDP pipelines)
Azure certifications (AI-102, DP-100, AZ-305) or relevant cloud AI credentials
Contributions to open-source AI frameworks or published technical writing
Experience working in startup or high-growth engineering environments
Passion for AI-native engineering transformation and modern software delivery practices
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