Senior Agentic AI Engineer

Job Overview:

Pay Range: $51.40hr - $66.71hr

Requirement/Must Have:

• Bachelor's degree in Computer Science or a related field (Master's preferred).
• 8+ years of relevant software engineering experience.
• 3+ years of hands-on experience developing Generative AI, machine learning, intelligent automation, or LLM-based applications, including substantial recent experience with Agentic AI.
• Strong programming proficiency in Python.
• Working knowledge of TypeScript/Node.js or a comparable language for application services.
• Strong software engineering background with experience designing and deploying production-grade cloud applications.
• Hands-on experience building Generative AI and RAG applications with Azure AI Foundry, Azure OpenAI, Azure AI Search, LLM APIs, embeddings, vector or hybrid search, knowledge retrieval, grounding, and citations.
• Experience with Agentic AI frameworks such as Microsoft Agent Framework, Semantic Kernel, LangGraph, AutoGen, or comparable frameworks, including single-agent and multi-agent systems, tool calling, MCP-based tool integration, and human-in-the-loop controls.
• Experience building natural language to SQL or conversational analytics solutions, including schema and metadata modeling, query generation, query validation, and grounding answers in retrieved data.
• Experience designing or contributing to decision intelligence systems that combine AI, analytics, business context, and operational workflows to improve decision quality and business outcomes.
• Experience evaluating and improving agent quality through prompt engineering, test datasets, LLM-based evaluation, safety checks, reasoning-quality assessment, and production feedback loops.
• Strong knowledge of LLMOps, CI/CD, Docker and Kubernetes, observability, and production operations for AI applications.
• Working knowledge of core Azure platform services: AKS or Azure Container Apps, Azure Functions, API Management, Entra ID, Key Vault, and Azure SQL or Cosmos DB.
• Good understanding of RESTful APIs, asynchronous patterns, secure integrations, relational databases, SQL, SQL/NoSQL data stores, and data engineering or ETL pipelines.
• Experience with enterprise-scale secure AI deployments, including identity, authorization, data privacy, compliance, and production monitoring.
• Strong analytical, problem-solving, collaboration, and communication skills.

Responsibilities:

• Design and build production-grade single-agent and multi-agent systems in Azure AI Foundry capable of orchestrating reasoning, planning, tool usage, and workflow execution.
• Architect scalable Generative AI solutions leveraging Azure AI Foundry, Azure OpenAI, Azure AI Search, and other enterprise AI services.
• Build and govern Retrieval-Augmented Generation (RAG) architectures using structured and unstructured enterprise data sources, including embeddings, vector/hybrid search, chunking, metadata filtering, reranking, grounding, and citations.
• Develop secure, reliable integrations between agents and enterprise systems using Model Context Protocol (MCP), REST APIs, relational databases, and event-driven services.
• Design Natural Language to SQL (NL2SQL) capabilities, ensuring accuracy, explainability, and secure access to enterprise data.
• Define semantic-layer strategies that enable AI agents to understand enterprise data models, business metrics, terminology, and relationships.
• Architect decision intelligence capabilities that combine enterprise data, business context, analytics, and AI reasoning to support informed decision-making.
• Establish reusable frameworks for impact analysis, dependency identification, prioritization, recommendation generation, and decision traceability.
• Ensure AI-generated recommendations are explainable, evidence-based, grounded in authoritative enterprise data, and aligned with business objectives.
• Define and automate evaluation methodologies for response quality, reasoning quality, recommendation relevance, business usefulness, and user trust, including golden datasets, LLM-based evaluation, and regression evals wired into CI/CD.
• Implement guardrails and Responsible AI controls: input/output content safety, PII protection, grounding checks, authorization boundaries, and human-in-the-loop escalation paths.
• Establish governance, security, safety, transparency, and compliance standards for enterprise AI solutions.
• Implementation of observability and monitoring frameworks for AI applications, including quality, performance, reliability, and adoption metrics, plus traces, tool calls, latency, token usage, and cost.
• Optimize solutions for cost and latency through model selection, prompt and context engineering, caching, and workload right-sizing.
• Drive architectural decisions for scalable, cloud-native AI platforms using modern software engineering, DevOps, and MLOps/LLMOps practices.
• Define technical standards, reference architecture, and reusable frameworks for AI agents, reasoning systems, and decision-support applications.
• Mentor engineers and provide technical framework on Agentic AI, decision intelligence, software architecture, and enterprise AI best practices.
• Partner with business stakeholders and domain experts to transform complex business requirements into reusable AI capabilities.
• Collaborate with data engineering teams to develop AI-ready data products, semantic models, metadata frameworks, and enterprise knowledge layers.
• Stay current with emerging AI technologies, frameworks, and industry practices, evaluating their applicability within the organization.

Nice to Have:

• Experience with Microsoft Fabric, Azure Databricks, or Azure Data Factory for AI-ready data pipelines.
• Exposure to Copilot Studio, Power Platform, or Teams-based agent experiences.
• Experience with model fine-tuning (e.g., LoRA/QLoRA), prompt caching, and token/cost optimization at scale.
• Supply chain domain knowledge (procurement, expediting, logistics, materials management) or familiarity with ERP data such as Oracle EBS or SAP.
• Front-end experience with React and TypeScript for building agent-facing user interfaces.
• Microsoft certifications such as Azure AI Engineer Associate (AI-102/AI-103) or Azure Solutions Architect (AZ-305).

Skills:

• Agentic AI.
• Generative AI.
• Decision Intelligence.
• Retrieval-Augmented Generation (RAG).
• Microsoft Azure.
• Azure AI Foundry.
• Azure OpenAI.
• Azure AI Search.
• Python.
• TypeScript.
• Node.js.
• Model Context Protocol (MCP).
• NL2SQL.
• LLMOps.
• MLOps.
• Docker.
• Kubernetes.
• AKS.
• Azure Functions.
• SQL.
• NoSQL.