AI SENIOR SOLUTIONS ARCHITECT

Kaleris • Alpharetta, GA

1d

About The Position

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, 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.

Requirements

• 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.
• 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.
• 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 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).
• 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).
• 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.

Nice To Haves

• 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.

Responsibilities

• 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).
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Benefits

• We encourage creativity, delight in innovation, and foster opportunities to grow.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior