Job Summary
Required Qualifications10+ years of experience across AI/ML, solution architecture, platform engineering, data engineering, enterprise automation, or cloud-native application delivery.Hands-on experience delivering production AI/ML, GenAI, RAG, conversational assistant, or agentic automation solutions at enterprise scale.Strong proficiency in at least one major cloud stack: Microsoft Azure, AWS, or Google Cloud Platform, including AI services, data services, identity/security, networking, and deployment patterns.Practical software engineering capability in Python and one or more of TypeScript, strong understanding of APIs, microservices, integration design, and testing strategies.Experience with LLMOps/MLOps practices such as evaluation, prompt/version management, model registry, monitoring, CI/CD, guardrails, and release governance.Knowledge of enterprise workflow and automation patterns across HR, Finance, Procurement, Supply Chain, or shared-services operations.Strong understanding of security, privacy, responsible AI, data residency, access control, audit logging, and model risk considerations.Client-facing consulting experience, including structured problem solving, executive communication, workshop facilitation, solution shaping, and delivery leadership.Preferred QualificationsExperience with managed-services or shared-services operating models, including process transition, service catalogues, SLAs, runbooks, knowledge management, and continuous improvement.Hands-on experience with agent frameworks and orchestration tools such as LangGraph, Semantic Kernel, AutoGen, CrewAI, OpenAI Assistants, or equivalent frameworks.Experience with automation and workflow platforms such as Microsoft Power Platform, UiPath, Automation Anywhere, ServiceNow, Camunda, Temporal, Airflow, or cloud-native workflow services.Experience integrating with ERP, HCM, procurement, and service-management platforms such as SAP, Oracle, Workday, Coupa, Ariba, ServiceNow, Dynamics, and Microsoft 365.Familiarity with vector databases and search technologies such as Azure AI Search, pgvector, Pinecone, Milvus, Redis, OpenSearch, Elasticsearch, or BigQuery/Vertex AI search patterns.Relevant cloud or architecture certifications such as Azure Solutions Architect, Azure AI Engineer, AWS Solutions Architect, AWS Machine Learning, Google Professional Cloud Architect, or Google Professional Machine Learning Engineer.Technical SkillsGenAI, Agents, and RAGLLM application design, RAG, embeddings, chunking, hybrid search, knowledge graphs, tool calling, function calling, planning, routing, memory, and multi-agent orchestration.Evaluation and safety: groundedness, relevancy, hallucination risk, regression testing, red teaming, policy enforcement, PII controls, content moderation, and prompt hardening.Cloud and PlatformGoogle Cloud: Vertex AI, Gemini, GKE, Cloud Run, Cloud Functions, BigQuery, Dataflow, Pub/Sub, Apigee, Cloud IAM, Secret Manager, Cloud Logging, and Cloud Monitoring.Engineering, Integration, and OperationsPython, TypeScript, REST/GraphQL APIs, event-driven architecture, containers, Kubernetes, Terraform/Bicep/CloudFormation, GitHub Actions/Azure DevOps/GitLab CI, and automated testing.Enterprise integration patterns for ERP, HCM, procurement, ITSM, CRM, document repositories, email/chat channels, OCR/document intelligence, and workflow engines.Observability and operations: OpenTelemetry, Prometheus/Grafana, cloud-native monitoring, Langfuse/Arize/WhyLabs or equivalents, incident management, SLAs/SLOs, and runbooks.Soft SkillsStrong executive presence with the ability to simplify complex AI, automation, and architecture topics for business and technology leaders.Hands-on leadership style: comfortable moving from strategy and architecture into code, prototyping, troubleshooting, and delivery problem solving.Outcome orientation with a strong focus on measurable value, adoption, operational resilience, and continuous im