Kaleris

Kaleris

AI SENIOR SOLUTIONS ARCHITECT

Alpharetta - HQ, USA · Senior

Sponsorship not specifiedDetected 82 days ago
PythonJavaC#FastAPIFlaskGitDatabricksVector DatabasesAWSAzureDockerKubernetesTerraformHelmCI/CDGitHub ActionsDevOpsAPI DevelopmentGraphQLRESTgRPCKafkaRabbitMQOAuth

About the role

  • Kaleris is an equal opportunity employer.
  • We celebrate diversity and are committed to creating an inclusive environment for all employees.

Responsibilities

  • 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.
  • Own AI project delivery from PoC to production, ensuring robust governance, risk management, security, and compliance.
  • 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.
  • Mentor and develop team members through training on AI frameworks, cloud development practices, and architectural patterns.
  • Initiate and lead rapid Proofs of Concept (PoCs) and Minimum Viable Products (MVPs) using AI and GenAI for streamlined business processes.

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).
  • 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.

Nice to have

  • 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.
  • Familiarity with AI agent frameworks (AutoGen, CrewAI, OpenAI Assistants API) and multi-agent orchestration patterns.
  • 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.

Skills

  • 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.
  • Cloud & Infrastructure
  • 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.
  • 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).

Benefits

  • Explore and pilot new AI features in LLMs, vision models, speech-to-text, translation, and personalization engines.

Equal opportunity

  • Microsoft Azure AI Engineer (AI-102), AWS Certified ML Specialty, Google Professional ML Engineer, or equivalent.
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This listing is sourced directly from Kaleris's careers page and normalized into a canonical job model.