Principal AI Data Architect - REMOTE / Contract (Need 15+ years)

Principal AI Data Architect

AI-Ready Data Platform ML/LLMOps Agentic AI Infrastructure Governance & Security Location: REMOTE
Department: Data & AI Engineering

About the Role

We are hiring a Principal AI Data Architect a hands-on, senior individual contributor responsible for designing, building, governing, and evolving the single source of truth that powers all AI initiatives across the organization.

This platform will serve as the foundational backbone for:

• Conversational AI assistants
• Dashboard intelligence
• Autonomous AI agents
• RAG-powered applications
• Predictive ML models

You will:

• Architect and implement the platform
• Define and enforce data contracts
• Govern access for both humans and AI agents
• Ensure accuracy, reliability, and traceability of AI outputs

Required Qualifications

Experience

• 15+ years in data engineering/architecture
• 3 5+ years in AI/ML/LLM data platforms
• Experience designing enterprise-scale AI platforms
• Strong background in regulated industries

Technical Skills

Expert:

• Python, SQL, PySpark
• Kafka, Databricks, Delta Lake, Snowflake
• AWS (S3, Glue, EKS, Bedrock, Kinesis, Redshift)
• Docker, Kubernetes, Terraform, CI/CD

Strong:

• LangChain, LlamaIndex
• LLM APIs (OpenAI, Bedrock, Claude, HuggingFace)
• Vector DBs (Pinecone, FAISS, ChromaDB, OpenSearch)
• Knowledge graphs (Neo4j)

Working Knowledge:

• MLflow, FastAPI
• Observability tools (Grafana, CloudWatch)
• Data lineage and metadata tools

Preferred Qualifications

• Degree in Computer Science or related field
• Experience in presales / solution architecture
• Background in financial services, SaaS, or regulated industries
• Familiarity with:
• MCP (Model Context Protocol)
• Agent frameworks (LangGraph, AutoGen, CrewAI)
• Experience with AI observability systems

Success Metrics (First 12 Months)

• AI platform architecture adopted within 120 days
• Platform serving 3+ AI consumer types
• Governance framework fully implemented
• Observability and evaluation systems operational
• Measurable improvement in AI output accuracy
• Legacy modernization delivering cost and speed benefits
• Engineering standards adopted organization-wide

Technology Stack

Databricks Delta Lake PySpark Kafka Snowflake AWS Azure Kubernetes Docker Terraform MLflow LangChain LlamaIndex OpenAI Bedrock Claude Pinecone FAISS ChromaDB OpenSearch Neo4j FastAPI Python SQL MCP LangGraph CI/CD Grafana CloudWatch

Key Responsibilities

1. AI-Ready Data Platform (Single Source of Truth)

• Architect and own the enterprise AI data platform
• Design multi-domain data models (lakehouse, data mesh, event-driven)
• Own full data stack:
• Streaming: Kafka, Spark Structured Streaming
• Batch: Databricks, PySpark, Delta Lake
• Cloud: AWS, Azure
• Eliminate data silos and ensure a unified data layer
• Modernize legacy ETL and DWH systems to cloud-native architectures

2. Semantic Models & Knowledge Layer

• Design semantic layer with:
• Ontologies, taxonomies, entity relationships
• Build and maintain knowledge graphs (e.g., Neo4j)
• Define feature store and semantic data contracts
• Ensure metadata management, lineage, and auditability

3. RAG, Vector & Retrieval Infrastructure

• Design embedding pipelines and vector stores:
• Pinecone, FAISS, ChromaDB, OpenSearch
• Define retrieval data contracts for AI systems
• Optimize for:
• Precision, recall, latency, and cost

4. ML/LLMOps Infrastructure

• Build ML and LLMOps pipelines:
• Training data pipelines
• Feature engineering
• Model registry (MLflow)
• Implement CI/CD for AI systems:
• Validation, deployment, rollback, monitoring
• Support LLM fine-tuning workflows:
• RLHF pipelines
• Data curation and filtering
• Establish best practices:
• Versioning, A/B testing, canary releases

5. Multi-Consumer AI Serving Architecture

Design data services for:

• Conversational AI
• Low-latency APIs for chatbots and copilots
• BI & Dashboard Assistants
• Semantic query layer and text-to-SQL
• Autonomous AI Agents
• Tool APIs, memory/state management
• Predictive ML Models
• Feature pipelines and real-time serving
• AI Experimentation
• Secure sandbox environments

6. Governance, Security & Access Control

• Implement RBAC and attribute-based access controls
• Enforce agent-specific permissions
• Ensure:
• PII masking
• Encryption
• Audit logging
• Compliance (SOX, GDPR, SOC2, AML/KYC)
• Define schema governance and versioning
• Maintain audit trails and data provenance

7. Agentic Observability & Output Accuracy

• Build observability for AI agents:
• Inputs, outputs, reasoning traces
• Define evaluation metrics:
• Accuracy, hallucination rate, relevance
• Create feedback loops to improve data quality
• Define SLAs for data freshness and AI accuracy
• Implement human-in-the-loop workflows

8. Architecture Standards & Enablement

• Define reference architecture and standards
• Establish:
• Testing frameworks
• CI/CD pipelines
• Infrastructure-as-Code (Terraform)
• Lead design reviews and architecture governance
• Conduct internal workshops and enablement sessions