Sr AI Developer

Senior AI Developer w/MS FabricBravotech, a leader in IT staffing and staff augmentation services, is seeking a talented Sr. AI Developer/ArchitectSkills: AI/ML Solutions, MS Stack, Azure, Azure Data Factory, MS Fabric, Fabric Data Factory, Fabric Data Pipelines, Fabric Semantic Models, Medallion Architecture, Python, MLOps, CI/CD, Leadership skills, Go-getter, Able to speak to business leaders in layman’s terms about projects.This role is ideal for a hands-on cloud AI engineer with deep expertise designing, deploying, securing, and optimizing enterprise AI platforms in Microsoft Azure environments.The ideal candidate will possess strong experience with Azure AI services, cloud-native AI architecture, MLOps, scalable deployment patterns, and enterprise-grade security and governance.Top SkillsMicrosoft Azure AI & Cloud Services Azure OpenAI Service Azure Machine Learning (Azure ML) Microsoft Fabric Azure Data Factory (ADF) Python AI/ML Deployment & MLOps Kubernetes / AKS GPU Workloads Cloud Security & Governance CI/CD Automation Infrastructure as Code (Terraform / Bicep) Key Responsibilities70% – Hands-On AI Development & Engineering30% – Business Partnership & Solution LeadershipAI Platform Architecture & DeploymentDesign, 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 retraining. 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. Architect production-ready pipelines from data ingestion through model monitoringDevelop 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 platforms (Azure, AWS, or GCP) to deploy secure, enterprise-grade solutionsEnsure governance, security, explainability, and responsible AI principles are embedded in every solutionOptimize performance, scalability, and cost-efficiency of AI workloads30% – Business Partnership & Solution LeadershipTranslate ambiguous business problems into structured AI solution designsPartner with business stakeholders (Finance, Operations, Sales, Marketing, IT) to identify high-value use casesClearly explain AI concepts, tradeoffs, and model outputs to non-technical audiencesLead solution design workshops and whiteboarding sessionsQuantify expected ROI and define measurable success metricsInfluence prioritization of AI initiatives based on business value and feasibilityMentor junior developers and help elevate AI literacy across the organizationAI Engineering & Cloud IntegrationIntegrate 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 & OptimizationImplement 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 & LeadershipPartner 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. Required Qualifications5+ 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. Must have 2+ years experience with MS FabricStrong Python scripting and automation skills. Experience with Infrastructure as Code tools such as Terraform, ARM Templates, or Bicep.70% – Hands-On AI Development & EngineeringDesign, build, and deploy scalable AI/ML solutions (predictive models, generative AI, NLP, optimization, automation)Architect production-ready pipelines from data ingestion through model monitoringDevelop 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 solutionsEnsure governance, security, explainability, and responsible AI principles are embedded in every solutionOptimize performance, scalability, and cost-efficiency of AI workloads30% – Business Partnership & Solution LeadershipTranslate ambiguous business problems into structured AI solution designsPartner with business stakeholders (Finance, Operations, Sales, Marketing, IT) to identify high-value use casesClearly explain AI concepts, tradeoffs, and model outputs to non-technical audiencesLead solution design workshops and whiteboarding sessionsQuantify expected ROI and define measurable success metricsInfluence prioritization of AI initiatives based on business value and feasibilityMentor junior developers and help elevate AI literacy across the organizationWhat Success Looks LikeAI solutions deployed into production that generate measurable business impactReduced time-to-value from idea to implementationBusiness leaders who trust and understand the AI solutions being deliveredScalable architecture that enables repeatable AI delivery across functionsRequired Qualifications7+ years of software development experience3+ years building and deploying machine learning or AI solutions in productionStrong 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 fundamentalsAbility to communicate complex technical ideas clearly and confidentlyDemonstrated experience working directly with business stakeholders4 days onsiteUSC or GCSalary w/benefit package