Artificial Intelligence Engineer

Position Title: Sr. AI Developer/Architect Location: 600 E. Las Colinas Blvd, Irving, TX 75039 Hours: Onsite Monday — Thursday, Remote Friday Duration: Full Time Experience Level: 10+ Years Key Skills AI/ML Solutions MS Stack Microsoft Azure AI & Cloud Services Azure Azure Data Factory / ADF Microsoft Fabric Python MLOps / AI/ML Deployment & MLOps CI/CD / CI/CD Automation Leadership skills Go-getter Azure OpenAI Service Azure Machine Learning (Azure ML) Kubernetes / AKS Infrastructure as Code (Terraform / Bicep) LLM Claude Code GPU Workloads Cloud Security & Governance AI Platform Architecture & Deployment Design, build, and deploy scalable AI/ML platforms in Microsoft Azure. Implement enterprise AI solutions using Azure OpenAI, Azure ML, Azure AI Services, and Microsoft Fabric. Architect end-to-end AI pipelines from data ingestion and preprocessing through model deployment, monitoring, and re‐training. Deploy GPU‑intensive AI workloads using Azure Kubernetes Service (AKS), Azure VM Scale Sets, or containerized cloud environments. Design highly available and fault‑tolerant AI infrastructure supporting enterprise‑scale workloads. AI Engineering & Cloud Integration Integrate AI solutions with Azure Data Factory, Azure Synapse, Microsoft Fabric, Databricks, and enterprise data platforms. Build MLOps pipelines for automated training, testing, deployment, and model lifecycle management. Implement CI/CD pipelines for AI solutions using Azure DevOps or GitHub Actions. Develop APIs and scalable inference endpoints for AI applications and LLM‑powered solutions. Security, Governance & Optimization Implement enterprise‑grade security controls for AI workloads including RBAC, Managed Identities, Key Vault, Private Endpoints, and network isolation. Ensure compliance with governance, data privacy, and responsible AI standards. Optimize AI cloud infrastructure for performance, scalability, and cost efficiency. Monitor AI workloads for latency, utilization, drift detection, and operational reliability. Collaboration & Leadership Partner with business stakeholders, architects, data engineers, and DevOps teams to deliver AI‑driven business solutions. Provide technical leadership on cloud AI architecture and deployment best practices. Evaluate emerging AI cloud technologies and recommend scalable enterprise solutions. 70% — Hands‑On AI Development & Engineering Design, build, and deploy scalable AI/ML solutions (predictive models, generative AI, NLP, optimization, automation). Architect production‑ready pipelines from data ingestion through model monitoring. Develop and fine‑tune LLM‑based applications (RAG architectures, prompt engineering, agents, copilots). Write high‑quality, production‑grade code (Python required; additional languages a plus). Implement MLOps best practices (CI/CD, model versioning, monitoring, drift detection). Work across cloud platform (Azure) to deploy secure, enterprise‑grade solutions. Ensure governance, security, explainability, and responsible AI principles are embedded in every solution. Optimize performance, scalability, and cost‑efficiency of AI workloads. 30% — Business Partnership & Solution Leadership Translate ambiguous business problems into structured AI solution designs. Partner with business stakeholders (Finance, Operations, Sales, Marketing, IT) to identify high‑value use cases. Clearly explain AI concepts, trade‑offs, and model outputs to non‑technical audiences. Lead solution design workshops and whiteboarding sessions. Quantify expected ROI and define measurable success metrics. Influence prioritization of AI initiatives based on business value and feasibility. Mentor junior developers and help elevate AI literacy across the organization. What Success Looks Like AI solutions deployed into production that generate measurable business impact. Reduced time‑to‑value from idea to implementation. Business leaders who trust and understand the AI solutions being delivered. Scalable architecture that enables repeatable AI delivery across functions. Required Qualifications 5+ years of experience in Azure cloud engineering, AI deployment, or ML platform engineering. Hands‑on experience with Azure Machine Learning, Azure OpenAI, Azure AI Services, and Microsoft Fabric. Strong experience deploying AI/ML models into production cloud environments. Experience with Kubernetes, Docker, and scalable GPU‑based deployments. Strong understanding of cloud networking, identity management, and security architecture. Experience implementing MLOps and CI/CD pipelines for AI systems. Strong Python scripting and automation skills. Experience with Infrastructure as Code tools such as Terraform and Bicep. 7+ years of software development experience. 3+ years building and deploying machine learning or AI solutions in production. Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit‑learn). Experience with LLM ecosystems (OpenAI APIs, Hugging Face, LangChain, vector databases). Solid understanding of data engineering fundamentals. Ability to communicate complex technical ideas clearly and confidently. Demonstrated experience working directly with business stakeholders. #J-18808-Ljbffr