Sr. AI Developer / AILEAD/ Architect

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