Senior Software Engineer - Agentic AI

Qualifications

• Proven experience building LLM-powered applications with Azure OpenAI, embeddings, vector stores, RAG, prompt engineering, and evaluation pipelines
• Hands-on 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, fallback strategies
• Practical use of Prompt Flow for authoring, testing, and deploying multi-step AI workflows in Azure AI Foundry
• Experience building and consuming MCP services to standardize tool access across agents
• Microsoft Data & App Platform
• Front-End & MTech Enterprise Stack
• Experience in TypeScript/Angular for operator consoles and human-in-the-loop oversight
• Ability to integrate with .

NET/C#, SQL Server, NServiceBus and Azure DevOps in our enterprise environment

• AI-Native Dev Workflow & Culture
• Seasoned aptitude for action, tight feedback loops, crisp written communication, and ownership mindset
• Quality & reliability: rising agent tool-use success rate; falling hallucination/retry rates; low incident volume; fast MTTR
• Performance & cost: P50/P95 latency and token-cost budgets met; measurable efficiency gains across services
• Engineering excellence: high test coverage, stable CI/CD, observable systems, and healthy on-call posture
• Services: Python (FastAPI, asyncio), .

NET/C#, REST/gRPC, containers, CI/CD with Azure DevOps

• Frontend: TypeScript/Angular, Ionic; E2E testing with Cypress

Responsibilities

• You will design, ship, and operate agentic systems that combine large language models (LLMs), tools/functions, planning, memory, evaluation, and multi-agent communication
• You will work primarily in Python for AI services and integrate with our enterprise stack (TypeScript/Angular, .

NET/C#, SQL Server, Azure), delivering trustworthy, cost-efficient, low-latency experiences in real customer workflows

• Build agentic AI applications on Azure AI Foundry: Azure OpenAI models, Prompt Flow, tools/function-calling, evaluations, vector search (Azure AI/Cognitive Search), and orchestration for multi-step reasoning and tool use
• Design memory & grounding: implement episodic/semantic/long-term memory with vector/graph stores; architect RAG pipelines and retrieval strategies that improve factuality and reduce latency/cost
• Integrate via Model Context Protocol (MCP) to standardize tool/skill access; design agent-to-agent communication, delegation, and event-driven workflows
• Connect agents to Microsoft Fabric (OneLake, Lakehouse, Warehouse, Real-Time Analytics) and Dataverse entities/workflows; ensure lineage, governance, and auditability
• Develop AI-native backend services in Python (FastAPI, asyncio) with evaluation harnesses, observability, and cost/latency/quality dashboards
• Embed AI features into the MTech stack: TypeScript/Angular UIs, .

NET/C# services, SQL Server, NServiceBus, Azure DevOps pipelines, and Ionic/Cypress where applicable

• Use AI-augmented development tools like GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding workflows to accelerate delivery, test generation, refactoring, and documentation
• Implement safety & reliability: guardrails, red-teaming, PII protection, prompt hardening, regression tests, automated evaluations; uphold SLO/SLA excellence in production
• Implement full cycle agentic engineering: design → model/tool selection → API & UI → deployment → monitoring → continuous improvement
• MCP, Memory & Agentic Communication
• Implemented memory architectures (episodic, semantic, vector, graph) and long-running conversational context
• Designed agent-to-agent communication patterns (messaging, orchestration, delegation, arbitration)
• Integration with Microsoft Fabric, SQL Server, Supabase, Databricks (OneLake/Lakehouse/Warehouse/Real-Time) for grounding data, retrieval, and telemetry
• Working knowledge of Dataverse entities, actions, and triggers; connecting agents to line-of-business records and Power Platform workflows
• Databricks for ELT, Delta Lake pipelines, feature engineering, ML training/serving, MLflow tracking and model lifecycle
• Azure IoT Hub/IoT Edge pipelines to incorporate device telemetry and edge-to-cloud intelligence into agentic workflows
• Azure services: App Service/Functions/AKS, Key Vault, Storage, Event Hubs/Service Bus, Monitor/Application Insights
• Python & Backend Engineering
• Production-grade Python (FastAPI, asyncio, type hints), Postgres/SQL, Redis, queues, OpenTelemetry, CI/CD, and containerization
• Strong API design, testing (unit/integration/property-based), performance tuning, and reliability engineering
• Daily use of GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding to speed delivery and raise quality
• Mentor teams in prompting, agent behavior design, context management, evaluation, and AI-assisted engineering practices
• AI & Agentic: Azure AI Foundry (Azure OpenAI, Prompt Flow, evaluations), MCP, Semantic Kernel, LangGraph, LangChain, AutoGen, CrewAI, HuggingFace embeddings, vector DBs, Azure AI/Cognitive Search, RAG, memory architectures
• Data & Integration: Databricks (ELT, ML, Delta Lake, MLflow), Microsoft Fabric (OneLake/Lakehouse/Warehouse/Real-Time), Dataverse, Event Hubs/Service Bus
• IoT: Azure IoT Hub, IoT Edge, stream ingestion & device telemetry flows
• AI-Native Dev Tools: GitHub Copilot, Bolt, Cursor, Replit, vibe-coding workflows

Full Description

We are hiring senior engineers who build fast, think AI-first, and can take agentic AI from prototype to production.

You will design, ship, and operate agentic systems that combine large language models (LLMs), tools/functions, planning, memory, evaluation, and multi-agent communication.

You will work primarily in Python for AI services and integrate with our enterprise stack (TypeScript/Angular,.

NET/C#, SQL Server, Azure), delivering trustworthy, cost-efficient, low-latency experiences in real customer workflows.

What You'll Do!

• Build agentic AI applications on Azure AI Foundry: Azure OpenAI models, Prompt Flow, tools/function-calling, evaluations, vector search (Azure AI/Cognitive Search), and orchestration for multi-step reasoning and tool use.

• Design memory & grounding: implement episodic/semantic/long-term memory with vector/graph stores; architect RAG pipelines and retrieval strategies that improve factuality and reduce latency/cost.

• Integrate via Model Context Protocol (MCP) to standardize tool/skill access; design agent-to-agent communication, delegation, and event-driven workflows.

• Connect agents to Microsoft Fabric (OneLake, Lakehouse, Warehouse, Real-Time Analytics) and Dataverse entities/workflows; ensure lineage, governance, and auditability.

• Develop AI-native backend services in Python (FastAPI, asyncio) with evaluation harnesses, observability, and cost/latency/quality dashboards.

• Embed AI features into the MTech stack: TypeScript/Angular UIs, .NET/C# services, SQL Server, NServiceBus, Azure DevOps pipelines, and Ionic/Cypress where applicable.

• Use AI-augmented development tools like GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding workflows to accelerate delivery, test generation, refactoring, and documentation.

• Implement safety & reliability: guardrails, red-teaming, PII protection, prompt hardening, regression tests, automated evaluations; uphold SLO/SLA excellence in production.

• Implement full cycle agentic engineering: design → model/tool selection → API & UI → deployment → monitoring → continuous improvement.

What You Bring!

Core AI & Agentic Expertise

• Proven experience building LLM-powered applications with Azure OpenAI, embeddings, vector stores, RAG, prompt engineering, and evaluation pipelines.

• Hands-on 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, fallback strategies.

• Practical use of Prompt Flow for authoring, testing, and deploying multi-step AI workflows in Azure AI Foundry.

MCP, Memory & Agentic Communication

• Experience building and consuming MCP services to standardize tool access across agents.

• Implemented memory architectures (episodic, semantic, vector, graph) and long-running conversational context.

• Designed agent-to-agent communication patterns (messaging, orchestration, delegation, arbitration).

Microsoft Data & App Platform

• Integration with Microsoft Fabric, SQL Server, Supabase, Databricks (OneLake/Lakehouse/Warehouse/Real-Time) for grounding data, retrieval, and telemetry.

• Working knowledge of Dataverse entities, actions, and triggers; connecting agents to line-of-business records and Power Platform workflows.

• Databricks for ELT, Delta Lake pipelines, feature engineering, ML training/serving, MLflow tracking and model lifecycle.

• Azure IoT Hub/IoT Edge pipelines to incorporate device telemetry and edge-to-cloud intelligence into agentic workflows.

• Azure services: App Service/Functions/AKS, Key Vault, Storage, Event Hubs/Service Bus, Monitor/Application Insights.

Python & Backend Engineering

• Production-grade Python (FastAPI, asyncio, type hints), Postgres/SQL, Redis, queues, OpenTelemetry, CI/CD, and containerization.

• Strong API design, testing (unit/integration/property-based), performance tuning, and reliability engineering.

Front-End & MTech Enterprise Stack

• Experience in TypeScript/Angular for operator consoles and human-in-the-loop oversight.

• Ability to integrate with .

NET/C#, SQL Server, NServiceBus and Azure DevOps in our enterprise environment.

AI-Native Dev Workflow & Culture

• Daily use of GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding to speed delivery and raise quality.

• Mentor teams in prompting, agent behavior design, context management, evaluation, and AI-assisted engineering practices.

• Seasoned aptitude for action, tight feedback loops, crisp written communication, and ownership mindset.

Success Looks Like (Outcomes)

• Quality & reliability: rising agent tool-use success rate; falling hallucination/retry rates; low incident volume; fast MTTR.

• Performance & cost: P50/P95 latency and token-cost budgets met; measurable efficiency gains across services.

• Adoption & impact: shipped features used by real users; clear business KPIs improved via automation/intelligence.

• Engineering excellence: high test coverage, stable CI/CD, observable systems, and healthy on-call posture.

Tooling & Stack Summary

• AI & Agentic: Azure AI Foundry (Azure OpenAI, Prompt Flow, evaluations), MCP, Semantic Kernel, LangGraph, LangChain, AutoGen, CrewAI, HuggingFace embeddings, vector DBs, Azure AI/Cognitive Search, RAG, memory architectures.

• Data & Integration: Databricks (ELT, ML, Delta Lake, MLflow), Microsoft Fabric (OneLake/Lakehouse/Warehouse/Real-Time), Dataverse, Event Hubs/Service Bus.

• IoT: Azure IoT Hub, IoT Edge, stream ingestion & device telemetry flows.

• Services: Python (FastAPI, asyncio), .NET/C#, REST/gRPC, containers, CI/CD with Azure DevOps.

• Frontend: TypeScript/Angular, Ionic; E2E testing with Cypress.

• AI-Native Dev Tools: GitHub Copilot, Bolt, Cursor, Replit, vibe-coding workflows.