Ai Senior Solutions Architect

Key Responsibilities

• 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, Graph
    
    QL, and event‑driven architectures.
  • Ensure compatibility with solutions running in AWS Sage Maker 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 Git Hub 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 product ionize 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, Tensor Flow, scikit‑learn) and GenAI orchestration tools (Lang Chain, 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 (Net Suite, 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, Num Py), and REST API development (FastAPI, Flask).
  • Working knowledge of Java, C#, or Go for enterprise integrations and micro‑services development.
  • Hands‑on experience with AI/ML frameworks:
    Tensor Flow, PyTorch, scikit‑learn, Hugging Face Transformers.
  • GenAI orchestration tools:
    Lang Chain, Semantic Kernel, Llama Index; experience…