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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** TASK FORCE LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Training Data, Artificial Intelligence, Data Analysis, Application Frameworks, Microsoft Azure, Cloud Engineering, Information Engineering, Data Security, Relational Databases, DevOps, Python (Programming Language), Machine Learning, Node.Js, Software Architecture, OpenAI, Software Deployment, Software Engineering, SQL Databases, Systems Integration, TypeScript, Enterprise Search, Enterprise Data Management, Microsoft Agent Framework, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Agentic-AI, AI Platforms, Information Technology, Low Latency, AutoGen, Machine Learning Operations, Restful APIs, Model Context Protocol, Semantic Kernel - **Published:** October 22, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pthnqhrcsv ## About the Role The successful candidate will possess a Bachelor's degree in Computer Science or a related field, with a preference for a Master's degree. A minimum of 8+ years of relevant software engineering experience is required, including at least 3+ years of hands-on experience with Generative AI, machine learning, intelligent automation, or large language model (LLM)-based applications. Strong proficiency in Python is essential, along with working knowledge of TypeScript/Node.js or similar languages for application development. Candidates should have extensive experience designing and deploying production AI applications on cloud platforms, particularly Azure. Familiarity with Azure AI Foundry, Azure OpenAI, Azure AI Search, and other enterprise AI services is critical. Experience with Agentic AI frameworks such as Microsoft Agent Framework, Semantic Kernel, LangGraph, or AutoGen, including multi-agent systems and tool integration, is highly desirable. Knowledge of natural language to SQL (NL2SQL), decision intelligence systems, and enterprise data modeling is essential. Candidates should demonstrate expertise in secure AI deployment, observability, MLOps/LLMOps, and compliance standards. Strong analytical, problem-solving, collaboration, and communication skills are required to succeed in this role. ## Description We are seeking a highly skilled Senior Agentic AI Engineer to join our team. This role involves designing, building, and operating production-grade Agentic AI, Decision Intelligence, and Retrieval-Augmented Generation solutions primarily on Microsoft Azure for our Digital Supply Chain Systems organization. The ideal candidate will be responsible for developing reusable, scalable, secure, and explainable enterprise AI capabilities that support diverse and evolving supply chain business requirements. You will work closely with cross-functional teams to architect innovative AI solutions that enhance decision-making, automate workflows, and improve operational efficiencies. The position offers an exciting opportunity to lead cutting-edge AI initiatives, influence enterprise AI strategy, and mentor engineering teams in best practices for Agentic AI, MLOps, and cloud-native architectures., Design and develop production-grade single-agent and multi-agent systems within Azure AI Foundry capable of orchestrating reasoning, planning, tool usage, and workflow execution. Architect scalable Generative AI solutions utilizing Azure AI Foundry, Azure OpenAI, Azure AI Search, and related services to meet enterprise needs. Build and govern Retrieval-Augmented Generation (RAG) architectures that leverage structured and unstructured enterprise data sources, including embeddings, vector/hybrid search, chunking, metadata filtering, reranking, grounding, and citations. Develop secure integrations between AI agents and enterprise systems via Model Context Protocol (MCP), REST APIs, relational databases, and event-driven services. Design Natural Language to SQL (NL2SQL) capabilities, ensuring accuracy, explainability, and secure data access. Define semantic-layer strategies to enable AI agents to understand enterprise data models, business metrics, terminology, and relationships. Architect decision intelligence systems that synthesize enterprise data, analytics, and AI reasoning to support informed decision-making. Establish reusable frameworks for impact analysis, dependency management, prioritization, recommendation generation, and decision traceability. Ensure AI-generated recommendations are explainable, evidence-based, and grounded in authoritative enterprise data aligned with business objectives. Implement evaluation methodologies to assess response quality, reasoning, relevance, and trustworthiness, integrating these into CI/CD pipelines. Enforce Responsible AI guardrails, including content safety, PII protection, grounding checks, and human-in-the-loop escalation paths. Establish governance, security, safety, transparency, and compliance standards for enterprise AI solutions. Implement observability and monitoring frameworks to track quality, performance, reliability, and adoption metrics, including traces, tool calls, latency, token usage, and costs. Optimize AI solutions for cost-efficiency and low latency through model selection, prompt engineering, caching, and workload right-sizing. Lead architectural decisions for scalable, cloud-native AI platforms using modern software engineering, DevOps, and MLOps/LLMOps practices. Define technical standards, reference architectures, and reusable frameworks for AI agents and decision-support applications. Mentor engineering teams and provide technical guidance on Agentic AI, decision intelligence, and enterprise AI best practices. Collaborate with business stakeholders and domain experts to translate complex requirements into reusable AI capabilities. Partner with data engineering teams to develop AI-ready data products, semantic models, and enterprise knowledge layers. Stay current with emerging AI technologies, evaluating their relevance for supply chain and enterprise environments.