About this role
Job Description:
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.
Required Qualifications
• 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.
Technical Requirements
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.
Desired Skills
• 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.
Kaleris is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.