Job Summary

• Define and own the enterprise AI platform architecture and roadmap.
• Design scalable AI ecosystems spanning Data, ML, GenAI, MLOps, and Engineering platforms.
• Establish architecture principles, guardrails, standards, and reference patterns.
• Drive platform modernization leveraging cloud-native AI services.

Key Responsibilities

Key Responsibilities

AI Platform Strategy & Architecture

• Define and own the enterprise AI platform architecture and roadmap.
• Design scalable AI ecosystems spanning Data, ML, GenAI, MLOps, and Engineering platforms.
• Establish architecture principles, guardrails, standards, and reference patterns.
• Drive platform modernization leveraging cloud-native AI services.

AI & GenAI Platform Engineering

• Architect solutions using Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent platforms.
• Design enterprise RAG, Agentic AI, Vector Database, and Knowledge Management architectures.
• Enable reusable AI services, APIs, accelerators, and frameworks.
• Build AI platforms supporting model development, fine-tuning, deployment, monitoring, and governance.

Skill Requirements

• AI/ML Platforms: Azure ML, Databricks, SageMaker, Vertex AI
• GenAI: Azure OpenAI, OpenAI, Anthropic, Gemini, Llama
• Frameworks: LangChain, LangGraph, Semantic Kernel, LlamaIndex
• Programming: Python, Java, Scala
• MLOps: MLflow, Kubeflow, Azure DevOps, GitHub Actions
• Cloud: Azure, AWS, GCP
• Data Platforms: Snowflake, Databricks, Synapse, BigQuery
• Vector Databases: Pinecone, Weaviate, ChromaDB, Azure AI Search
• Containerization: Docker, Kubernetes
• APIs & Integration: REST, GraphQL, API Management

Functional Skills

• Enterprise Architecture
• AI Governance & Responsible AI
• Technology Strategy & Roadmap
• Solution Design
• Stakeholder Management
• Presales & Consulting

Other Requirements