Generative AI Platform Manager, Vice President
$120,000 - $202,500 Annual
ApplyGenerative AI Platform Manager, Vice President
Posted 19 days ago
SENIORITY
Manager
SALARY
$120,000 - $202,500 Annual
About the role
- AWS Bedrock Platform (Primary)
- Own enterprise Bedrock strategy: model access governance, provisioned throughput, cross-region inference, multi-account architecture
- Operationalize Knowledge Bases, Agents, Guardrails, Prompt Management, Flows, and Model Evaluation
- Lead FM lifecycle across Claude, Nova/Titan, Llama, Mistral, and CohereDesign RAG on Bedrock with OpenSearch Serverless, Aurora pgvector, and Kendra
- Optimize consumption: on-demand vs. PT, model routing, prompt caching, token efficiency
- Azure AI Foundry & Azure OpenAI (Primary)
- Own Foundry hub/project architecture, model catalog governance, and quota management
- Lead Azure OpenAI patterns (PTU vs. PAYG, capacity planning, content filters) across GPT-4o, GPT-4.1, and o-series models
- Architect RAG/agent workloads with AI Search, prompt flow, and Agent Service
- Implement Content Safety, private networking, Entra ID, CMK, and data residency controls
- Databricks & Data-Centric AI (Primary)
- Own Databricks strategy on AWS and Azure: workspace architecture, Unity Catalog, cluster policies
- Lead Mosaic AI adoption: Model Serving, Vector Search, Feature Store, AI Gateway, MLflowArchitect fine-tuning/pretraining pipelines and lakehouse-native RAG on Delta Lake
- Establish DBU cost controls and serverless governance
- Infrastructure as Code & Golden PathsAuthor L2/L3 CDK constructs (TypeScript/Python), Bicep modules, and Terraform for multi-cloud AI infra
- Codify secure-by-default "golden paths" enabling teams to launch Gen AI workloads in hours
- Standardize config-as-code, secrets management, and drift detection
- Harness CI/CDOwn Harness pipelines, templates, and delegates for Bedrock, Azure OpenAI, Foundry, and Databricks Asset Bundles
- Integrate with GitOps, Terraform/CDK/Bicep, OPA policy-as-code, and approval gates
- Establish reference pipelines with linting, security scanning, model eval, and cost checks
- Developer Experience
- Champion Claude Desktop, MCP servers, and AI coding assistants (Copilot, Cursor, Claude Code)
- Build internal MCP servers exposing enterprise systems to agentic clients
- Define secure usage patterns for regulated environments; measure productivity impact
- Governance & Leadership
- Establish security, compliance, and responsible AI controls (red-teaming, audit logging, guardrails)
- Build observability across CloudWatch, Azure Monitor, Databricks system tables, Grafana, Datadog
- Partner with Data, MLOps, Security, and Application leadership; recruit and mentor a top-tier team
- RequiredSkillsBedrock: Knowledge Bases, Agents, Guardrails, Flows, PT; Claude, Nova, Llama, Mistral; RAG and vector stores (OpenSearch, pgvector, Pinecone, Kendra); agent frameworks (Bedrock Agents, LangGraph, Strands)
- Azure AI: Foundry hubs/projects, prompt flow, Agent Service; Azure OpenAI (GPT-4o/4.1, o-series, PTU); AI Search; Content Safety
- Published CDK construct libraries, Bicep registries, or Terraform modules consumed enterprise-wide
- Enterprise-scale Harness pipeline authoring and rollout
- Fine-tuning/continued pretraining experience (LoRA, QLoRA, RLHF, DPO) on Mosaic AI, Azure OpenAI, or Bedrock Custom ModelsModel optimization: quantization, distillation, prompt caching, speculative decodingPTU/PT capacity planning at scale
- Production MCP servers and Claude Desktop deployments in regulated environments
- Agentic frameworks (Bedrock Agents, Azure AI Agent Service, Databricks Agent Framework, LangGraph, Strands)AI safety/guardrail frameworks (Bedrock Guardrails, Azure Content Safety, NeMo, Guardrails AI)
- Measurable FinOps wins (token/DBU/inference cost reduction)
- Open-source contributions (CDK, Terraform providers, MCP)
- Multi-tenant AI platforms with chargeback/showback
- Board/regulator-level presentation experience
- (one or more preferred)AWS: Solutions Architect Pro, ML Specialty, DevOps Engineer ProAzure: AZ-305, AI-102, AZ-400, OP-100 Databricks: Data Engineer Pro, ML Pro, or Generative AI Engineer AssociateGCP: Professional Cloud Architect or ML Engineer (complementary)
- Harness: Certified Expert (CD or Platform)
- Anthropic: Claude Builder or partner credentials
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
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