AI & Enterprise Application Strategy

• Define an AI/ML adoption roadmap across ERP, CRM, HRIS, BI, and custom applications.
• Translate strategic objectives into use-case-driven AI initiatives, leveraging GenAI capabilities for tangible business value.
• Advise IT leadership on emerging AI trends, frameworks, and platform innovations (e.g., LLM orchestration, multi-modal AI).

Architecture & Integration

• Architect end-to-end AI solutions in Microsoft Azure AI, integrating with enterprise systems via REST APIs, GraphQL, and event-driven architectures.
• Ensure compatibility with solutions running in AWS SageMaker and hybrid-cloud deployments.
• Assist with design data ingestion and preparation pipelines.

CI/CD, MLOps & Team Leadership

• Lead a team of engineers and data scientists in delivering complex AI projects (e.g., document intelligence, NLP chatbots, predictive analytics, RPA workflows).
• Implement MLOps practices and CI/CD pipelines using GitHub Actions for AI model lifecycle management.
• Establish model monitoring, retraining schedules, and drift detection with frameworks like MLflow and Kubeflow.

Project Delivery

• Own AI project delivery from PoC to production, ensuring robust governance, risk management, security, and compliance.
• Deploy scalable models in Azure AI Studio and productionize via APIs or microservices in Kubernetes/AKS.

Stakeholder & Vendor Engagement

• Collaborate with Business Analysts, Product Owners, Developers, and Data Engineers to ensure solutions meet functional and performance requirements.
• Partner with external AI vendors, cloud providers, and technology partners to align on deliverables and integrations.

Technical Excellence

• Hands-on evaluation and selection of AI/ML frameworks (PyTorch, TensorFlow, scikit-learn) and GenAI orchestration tools (LangChain, Semantic Kernel).
• Review and approve solution architecture and code for scalability, efficiency, and security compliance.
• Mentor and develop team members through training on AI frameworks, cloud development practices, and architectural patterns.

Governance & Security

• Assist with implementation of AI-specific data governance, privacy policies, and responsible AI principles.
• Ensure compliance with standards and regulations (GDPR, SOC 2, ISO 27001) and practices such as OAuth2, SAML, RBAC/ABAC, encryption-at-rest/in-transit.

Innovation

• Initiate and lead rapid Proofs of Concept (PoCs) and Minimum Viable Products (MVPs) using AI and GenAI for streamlined business processes.
• Explore and pilot new AI features in LLMs, vision models, speech-to-text, translation, and personalization engines.

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML Engineering, or a related technical field.
• 5+ years in enterprise IT/applications management with at least 5+ years in AI/ML solution delivery in production environments.
• Proven track record leading cross-functional technical teams on complex AI/ML projects in diverse, matrixed enterprise environments.
• Deep experience with enterprise application platforms including CRM (Salesforce), ERP (NetSuite, SAP, Oracle), HRIS (Workday), and PSA/Billing (Certinia).
• Demonstrated expertise in GenAI, NLP, RPA, predictive modeling, computer vision, and recommendation systems.
• Strong understanding of enterprise integration patterns, event-driven architecture, and data engineering principles.
• Experience working in regulated or compliance-sensitive environments (SOC 2, GDPR, ISO 27001).
• Ability to balance hands-on technical delivery with strategic planning and executive-level communication.
• Strong project ownership and accountability with experience in end-to-end delivery from requirements through post-production support.

Languages & Frameworks

• Advanced Python proficiency including async patterns, data manipulation (pandas, NumPy), and REST API development (FastAPI, Flask).
• Working knowledge of Java, C#, or Go for enterprise integrations and microservices development.
• Hands-on experience with AI/ML frameworks: TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers.
• GenAI orchestration tools: LangChain, Semantic Kernel, LlamaIndex; experience with prompt engineering and RAG architecture design.

Cloud & Infrastructure

• Expertise in cloud-native architecture on Microsoft Azure: Azure AI Studio, Azure Machine Learning, Azure OpenAI Service, Azure Data Factory, Synapse Analytics, AKS, Azure Functions.
• Hands-on experience with AWS ML services: SageMaker, Bedrock, Lambda, S3, and hybrid-cloud deployment patterns.
• Container orchestration: Kubernetes (AKS/EKS), Docker, Helm charts for ML model deployment.
• Infrastructure-as-Code: Terraform, Bicep, or ARM templates for reproducible environment provisioning.

Integration & Data

• Integration patterns: REST APIs, gRPC, GraphQL, message queues (Kafka, Azure Service Bus, RabbitMQ), and webhook-based architectures.
• Data streaming and batch pipeline design using Azure Data Factory, Databricks, Synapse Analytics, and Spark.
• Experience designing vector databases and embedding pipelines for RAG/semantic search (Azure AI Search, Pinecone, Weaviate).
• Familiarity with data lakehouse patterns and medallion architecture (Bronze/Silver/Gold).

MLOps & DevSecOps

• CI/CD pipeline implementation for AI/ML workloads using Azure DevOps, GitHub Actions, or Jenkins.
• MLOps platforms: MLflow, Kubeflow, Azure ML Pipelines including model registry, versioning, and experiment tracking.
• Model monitoring, drift detection, and automated retraining pipelines.
• Security tooling: IAM/RBAC, OAuth2/SAML implementation, encryption-at-rest and in-transit, vulnerability scanning (Snyk, Dependabot).

Automation & RPA

• Experience with process automation platforms: Power Automate, UiPath, Blue Prism including AI-augmented workflow design.
• Familiarity with Microsoft Power Platform (Power Apps, Power Automate, Copilot Studio) for low-code AI integration.

• Exceptional communication across technical and executive levels — able to translate complex AI concepts into business value narratives.
• Demonstrated track record in change management for enterprise AI adoption, including stakeholder readiness, training, and cultural enablement.
• Advanced problem-solving skills, particularly in scaling AI workloads from prototype to production under enterprise constraints.
• Ability to architect AI reference patterns, reusable components, and drive enterprise-wide standards adoption.
• Experience building and presenting business cases for AI investments, including ROI modeling, TCO analysis, and risk framing.
• Familiarity with AI agent frameworks (AutoGen, CrewAI, OpenAI Assistants API) and multi-agent orchestration patterns.
• Exposure to AI governance frameworks (NIST AI RMF, EU AI Act, Microsoft Responsible AI Standard) and enterprise AI policy design.
• Experience with Salesforce Einstein, Agentforce, or Salesforce AI capabilities a plus given enterprise CRM environment.
• Contributions to open-source AI projects, published research, or conference presentations a distinguishing factor.
• Relevant certifications: Microsoft Azure AI Engineer (AI-102), AWS Certified ML Specialty, Google Professional ML Engineer, or equivalent.