{"schemaVersion":"jobsearcher.job.v1","id":"6c1842ab048492d6c37da152","url":"https://jobsearcher.com/jobs/6c1842ab048492d6c37da152","canonicalUrl":"https://jobsearcher.com/jobs/6c1842ab048492d6c37da152","title":"Senior Software Engineer - Agentic AI - Python Expert","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.\r\nWhat You'll Do!\r\nBuild 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.\r\nDesign 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.\r\nIntegrate via Model Context Protocol (MCP) to standardize tool/skill access; design agent-to-agent communication, delegation, and event-driven workflows.\r\nConnect agents to Microsoft Fabric (OneLake, Lakehouse, Warehouse, Real-Time Analytics) and Dataverse entities/workflows; ensure lineage, governance, and auditability.\r\nDevelop AI-native backend services in Python (FastAPI, asyncio) with evaluation harnesses, observability, and cost/latency/quality dashboards.\r\nEmbed AI features into the Speria stack: TypeScript/Angular UIs, .NET/C# services, SQL Server, NServiceBus, Azure DevOps pipelines, and Ionic/Cypress where applicable.\r\nUse AI-augmented development tools like GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding workflows to accelerate delivery, test generation, refactoring, and documentation.\r\nImplement safety & reliability: guardrails, red-teaming, PII protection, prompt hardening, regression tests, automated evaluations; uphold SLO/SLA excellence in production.\r\nImplement full cycle agentic engineering: design ? model/tool selection ? API & UI ? deployment ? monitoring ? continuous improvement.\r\nWhat You Bring!\r\nCore AI & Agentic Expertise\r\nProven experience building LLM-powered applications with Azure OpenAI, embeddings, vector stores, RAG, prompt engineering, and evaluation pipelines.\r\nHands-on with agent frameworks such as Semantic Kernel, LangGraph, LangChain Agents, AutoGen, or CrewAI.\r\nAbility to design deterministic, evaluatable, and safe agent behaviors including function schemas, tool success metrics, fallback strategies.\r\nPractical use of Prompt Flow for authoring, testing, and deploying multi-step AI workflows in Azure AI Foundry.\r\nMCP, Memory & Agentic Communication\r\nExperience building and consuming MCP services to standardize tool access across agents.\r\nImplemented memory architectures (episodic, semantic, vector, graph) and long-running conversational context.\r\nDesigned agent-to-agent communication patterns (messaging, orchestration, delegation, arbitration).\r\nMicrosoft Data & App Platform\r\nIntegration with Microsoft Fabric, SQL Server, Supabase, Databricks (OneLake/Lakehouse/Warehouse/Real-Time) for grounding data, retrieval, and telemetry.\r\nWorking knowledge of Dataverse entities, actions, and triggers; connecting agents to line-of-business records and Power Platform workflows.\r\nDatabricks for ELT, Delta Lake pipelines, feature engineering, ML training/serving, MLflow tracking and model lifecycle.\r\nAzure IoT Hub/IoT Edge pipelines to incorporate device telemetry and edge-to-cloud intelligence into agentic workflows.\r\nAzure services: App Service/Functions/AKS, Key Vault, Storage, Event Hubs/Service Bus, Monitor/Application Insights.\r\nPython & Backend Engineering\r\nProduction-grade Python (FastAPI, asyncio, type hints), Postgres/SQL, Redis, queues, OpenTelemetry, CI/CD, and containerization.\r\nStrong API design, testing (unit/integration/property-based), performance tuning, and reliability engineering.\r\nFront-End & Speria Enterprise Stack\r\nExperience in TypeScript/Angular for operator consoles and human-in-the-loop oversight.\r\nAbility to integrate with .NET/C#, SQL Server, NServiceBus and Azure DevOps in our enterprise environment.\r\nAI-Native Dev Workflow & Culture\r\nDaily use of GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding to speed delivery and raise quality.\r\nMentor teams in prompting, agent behavior design, context management, evaluation, and AI-assisted engineering practices.\r\nSeasoned aptitude for action, tight feedback loops, crisp written communication, and ownership mindset.\r\nSuccess Looks Like (Outcomes)\r\nQuality & reliability: rising agent tool-use success rate; falling hallucination/retry rates; low incident volume; fast MTTR.\r\nPerformance & cost: P50/P95 latency and token-cost budgets met; measurable efficiency gains across services.\r\nAdoption & impact: shipped features used by real users; clear business KPIs improved via automation/intelligence.\r\nEngineering excellence: high test coverage, stable CI/CD, observable systems, and healthy on-call posture.\r\nTooling & Stack Summary\r\nAI & 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.\r\nData & Integration: Databricks (ELT, ML, Delta Lake, MLflow), Microsoft Fabric (OneLake/Lakehouse/Warehouse/Real-Time), Dataverse, Event Hubs/Service Bus.\r\nIoT: Azure IoT Hub, IoT Edge, stream ingestion & device telemetry flows.\r\nServices: Python (FastAPI, asyncio), .NET/C#, REST/gRPC, containers, CI/CD with Azure DevOps.\r\nFrontend: TypeScript/Angular, Ionic; E2E testing with Cypress.\r\nAI-Native Dev Tools: GitHub Copilot, Bolt, Cursor, Replit, vibe-coding workflows.\r\nJ-18808-Ljbffr","company":"Socket","rawCompany":"socket","city":"Atlanta","state":"GA","isRemote":false,"isActive":false,"createdAt":"2026-08-08T01:49:41.063Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1251.00","title":"Computer Programmers","slug":"computer-programmers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Software Engineer - Agentic AI - Python Expert","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.\r\nWhat You'll Do!\r\nBuild 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.\r\nDesign 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.\r\nIntegrate via Model Context Protocol (MCP) to standardize tool/skill access; design agent-to-agent communication, delegation, and event-driven workflows.\r\nConnect agents to Microsoft Fabric (OneLake, Lakehouse, Warehouse, Real-Time Analytics) and Dataverse entities/workflows; ensure lineage, governance, and auditability.\r\nDevelop AI-native backend services in Python (FastAPI, asyncio) with evaluation harnesses, observability, and cost/latency/quality dashboards.\r\nEmbed AI features into the Speria stack: TypeScript/Angular UIs, .NET/C# services, SQL Server, NServiceBus, Azure DevOps pipelines, and Ionic/Cypress where applicable.\r\nUse AI-augmented development tools like GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding workflows to accelerate delivery, test generation, refactoring, and documentation.\r\nImplement safety & reliability: guardrails, red-teaming, PII protection, prompt hardening, regression tests, automated evaluations; uphold SLO/SLA excellence in production.\r\nImplement full cycle agentic engineering: design ? model/tool selection ? API & UI ? deployment ? monitoring ? continuous improvement.\r\nWhat You Bring!\r\nCore AI & Agentic Expertise\r\nProven experience building LLM-powered applications with Azure OpenAI, embeddings, vector stores, RAG, prompt engineering, and evaluation pipelines.\r\nHands-on with agent frameworks such as Semantic Kernel, LangGraph, LangChain Agents, AutoGen, or CrewAI.\r\nAbility to design deterministic, evaluatable, and safe agent behaviors including function schemas, tool success metrics, fallback strategies.\r\nPractical use of Prompt Flow for authoring, testing, and deploying multi-step AI workflows in Azure AI Foundry.\r\nMCP, Memory & Agentic Communication\r\nExperience building and consuming MCP services to standardize tool access across agents.\r\nImplemented memory architectures (episodic, semantic, vector, graph) and long-running conversational context.\r\nDesigned agent-to-agent communication patterns (messaging, orchestration, delegation, arbitration).\r\nMicrosoft Data & App Platform\r\nIntegration with Microsoft Fabric, SQL Server, Supabase, Databricks (OneLake/Lakehouse/Warehouse/Real-Time) for grounding data, retrieval, and telemetry.\r\nWorking knowledge of Dataverse entities, actions, and triggers; connecting agents to line-of-business records and Power Platform workflows.\r\nDatabricks for ELT, Delta Lake pipelines, feature engineering, ML training/serving, MLflow tracking and model lifecycle.\r\nAzure IoT Hub/IoT Edge pipelines to incorporate device telemetry and edge-to-cloud intelligence into agentic workflows.\r\nAzure services: App Service/Functions/AKS, Key Vault, Storage, Event Hubs/Service Bus, Monitor/Application Insights.\r\nPython & Backend Engineering\r\nProduction-grade Python (FastAPI, asyncio, type hints), Postgres/SQL, Redis, queues, OpenTelemetry, CI/CD, and containerization.\r\nStrong API design, testing (unit/integration/property-based), performance tuning, and reliability engineering.\r\nFront-End & Speria Enterprise Stack\r\nExperience in TypeScript/Angular for operator consoles and human-in-the-loop oversight.\r\nAbility to integrate with .NET/C#, SQL Server, NServiceBus and Azure DevOps in our enterprise environment.\r\nAI-Native Dev Workflow & Culture\r\nDaily use of GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding to speed delivery and raise quality.\r\nMentor teams in prompting, agent behavior design, context management, evaluation, and AI-assisted engineering practices.\r\nSeasoned aptitude for action, tight feedback loops, crisp written communication, and ownership mindset.\r\nSuccess Looks Like (Outcomes)\r\nQuality & reliability: rising agent tool-use success rate; falling hallucination/retry rates; low incident volume; fast MTTR.\r\nPerformance & cost: P50/P95 latency and token-cost budgets met; measurable efficiency gains across services.\r\nAdoption & impact: shipped features used by real users; clear business KPIs improved via automation/intelligence.\r\nEngineering excellence: high test coverage, stable CI/CD, observable systems, and healthy on-call posture.\r\nTooling & Stack Summary\r\nAI & 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.\r\nData & Integration: Databricks (ELT, ML, Delta Lake, MLflow), Microsoft Fabric (OneLake/Lakehouse/Warehouse/Real-Time), Dataverse, Event Hubs/Service Bus.\r\nIoT: Azure IoT Hub, IoT Edge, stream ingestion & device telemetry flows.\r\nServices: Python (FastAPI, asyncio), .NET/C#, REST/gRPC, containers, CI/CD with Azure DevOps.\r\nFrontend: TypeScript/Angular, Ionic; E2E testing with Cypress.\r\nAI-Native Dev Tools: GitHub Copilot, Bolt, Cursor, Replit, vibe-coding workflows.\r\nJ-18808-Ljbffr","datePosted":"2026-08-08T01:49:41.063Z","dateModified":"2026-08-08T01:49:41.063Z","hiringOrganization":{"@type":"Organization","name":"Socket","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Atlanta","addressRegion":"GA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"6c1842ab048492d6c37da152"},"url":"https://jobsearcher.com/jobs/6c1842ab048492d6c37da152"}}