Senior Software Engineer - Agentic AI
Uses vibe-coding workflows and AI-augmented tools (Cursor, GitHub Copilot, Bolt, Replit) to accelerate development and testing.
About the Role
Lead design, implementation, and operation of agentic AI systems that combine LLMs, tools, planning, memory, evaluation, and multi-agent communication. Build AI services primarily in Python and integrate them into an enterprise stack (TypeScript/Angular, .NET/C#, SQL Server, Azure) to deliver trustworthy, low-latency production experiences.
Job Description
Role
Senior Software Engineer focused on agentic AI: design, ship, and operate production-grade agentic systems that combine LLMs, tool/function calling, planning, memory, evaluation, and multi-agent communication. Work primarily in Python for AI services and integrate with the company’s enterprise stack to deliver reliable, cost-efficient, low-latency solutions in real customer workflows.
Key Responsibilities
- Build agentic AI applications on Azure AI Foundry using Azure OpenAI, Prompt Flow, tool/function-calling, evaluations, vector search, and orchestration for multi-step reasoning.
- Design and implement memory and grounding solutions (episodic/semantic/long-term) using vector/graph stores, and architect RAG pipelines and retrieval strategies to improve factuality and reduce latency/cost.
- Standardize tool/skill access via Model Context Protocol (MCP) and design agent-to-agent communication, delegation, and event-driven workflows.
- Connect agents to Microsoft data platforms (Microsoft Fabric, Dataverse, Databricks) ensuring lineage, governance, and auditability.
- Develop backend AI-native services in Python (FastAPI, asyncio) with evaluation harnesses, observability, and dashboards for cost/latency/quality.
- Embed AI features into TypeScript/Angular UIs and .NET/C# services, and integrate with SQL Server, NServiceBus, and Azure DevOps pipelines.
- Use AI-augmented development tools (GitHub Copilot, Bolt, Cursor, Replit) and vibe-coding workflows to accelerate delivery and improve code quality.
- Implement safety and reliability measures: guardrails, red-teaming, PII protection, prompt hardening, regression tests, automated evaluations, and uphold SLO/SLA standards.
- Drive full lifecycle agentic engineering including model/tool selection, API design, UI deployment monitoring, and continuous improvement.
Requirements
- Proven experience building LLM-powered applications with Azure OpenAI, embeddings, vector stores, RAG, prompt engineering, and evaluation pipelines.
- Hands-on experience with agent frameworks such as Semantic Kernel, LangGraph, LangChain Agents, AutoGen, or CrewAI.
- Ability to design deterministic, evaluatable, and safe agent behaviors including function schemas, tool success metrics, and fallback strategies.
- Practical experience with Prompt Flow for authoring, testing, and deploying multi-step AI workflows.
- Experience building and consuming MCP services, memory architectures (episodic, semantic, vector, graph), and agent-to-agent communication patterns.
- Integration experience with Microsoft Fabric, Databricks, Dataverse, Supabase, and related data platforms for grounding and telemetry.
- Familiarity with Azure services (App Service/Functions/AKS, Key Vault, Storage, Event Hubs/Service Bus, Monitor/Application Insights) and Azure IoT Hub/IoT Edge for device telemetry scenarios.
- Production-grade Python skills (FastAPI, asyncio, type hints), Postgres/SQL, Redis, queues, OpenTelemetry, CI/CD, containerization, API design, and testing practices.
- Experience with TypeScript/Angular for operator consoles and integration with .NET/C#, SQL Server, NServiceBus, and Azure DevOps.
- Strong mentoring, communication, ownership, and on-call reliability practices.
Tech Stack (explicit)
Azure AI Foundry, Azure OpenAI, Prompt Flow, Azure AI/Cognitive Search, MCP, Semantic Kernel, LangGraph, LangChain, AutoGen, CrewAI, HuggingFace embeddings, vector DBs, RAG, Databricks, Microsoft Fabric (OneLake/Lakehouse/Warehouse/Real-Time), Dataverse, Supabase, Azure IoT Hub, IoT Edge, App Service, Azure Functions, AKS, Key Vault, Storage, Event Hubs, Service Bus, Monitor/Application Insights, Python (FastAPI, asyncio), Postgres, SQL Server, Redis, OpenTelemetry, NServiceBus, .NET/C#, TypeScript/Angular, Ionic, Cypress, GitHub Copilot, Bolt, Cursor, Replit, MLflow, Delta Lake.
Location & Work Model
Hybrid role based in Dunwoody.
Outcomes / Success Measures
- Improved agent tool-use success rates and reduced hallucination/retry rates.
- Meeting latency (P50/P95) and token-cost budgets with measurable efficiency gains.
- Shipped features adopted by real users and demonstrable KPI improvements.
- High test coverage, stable CI/CD, observable systems, and healthy on-call posture.