AI Data Architect

Team: Product Development

Location: United States

Commitment: Full-time

Workplace Type: remote

Requirements:

Architect and own the enterprise AI data platform — the unified, governed layer that ingests, transforms, stores, and serves all data consumed by AI systems across the organisation.
Design multi-domain data models (lakehouse, data mesh, event-driven) that are structured from day one to serve AI workloads: clean lineage, versioned schemas, well-documented contracts, and low-latency serving APIs.
Strong exposure to different Data architectures, data lake & data warehouse
Define tools & technologies to develop automated data pipelines, write ETL processes, develop dashboard & report and create insights

Responsibilities

Technical Skills
Primary Skills: Python, SQL, Snowflake/Databricks, AWS (S3, Glue, EKS, Bedrock, Kinesis, Redshift), Docker, Kubernetes, Terraform, GitHub Actions, LangChain, LlamaIndex, LLM APIs (OpenAI, AWS Bedrock, Claude, HuggingFace), (Pinecone, FAISS, ChromaDB, OpenSearch), knowledge graphs (Neo4j).
 
Secondary Skills: MLflow, FastAPI, CI/CD pipelines, observability tooling (CloudWatch, Grafana, or equivalent), data lineage and metadata management platforms.
 
• 15+ years of hands-on data engineering and architecture experience, alongside building production AI/ML and LLM-era data infrastructure.
• Strong Experience with either Databricks or Snowflake; experience with both is desirable.
• Strong data architecture patterns & principles, ability to design secure & scalable data lakes, data warehouse, data hubs, and other event-driven architectures
• Expertise in designing and writing ETL processes in Python / Java / Scala
• Own the full data stack: real-time streaming (Kafka, Spark Structured Streaming), batch processing (Databricks, PySpark, Delta Lake), cloud storage and compute (AWS, Azure), and data quality /metadata management.
• Drive modernisation of legacy pipelines (on-prem ETL, batch DWH) to cloud-native, AI-ready architectures with measurable improvements in cost, latency, and delivery velocity.
• Proven experience designing enterprise-scale AI data platforms that serve multiple AI consumers —not just one application or pipeline.
• Hands-on experience with vector stores, semantic models, knowledge graphs, and retrieval infrastructure in production environments.
• Working knowledge of LLMOps: model serving pipelines, MLflow, CI/CD for AI, automated evaluation, and production monitoring.
   
   
  
  

AI Experience

RAG, Vector & Retrieval Infrastructure
Design the retrieval infrastructure that powers RAG-based AI applications: embedding pipelines, vector stores (Pinecone, FAISS, ChromaDB, OpenSearch), chunking strategies, and hybrid retrieval layers combining semantic search with structured queries.
 
Agentic Behaviour Observability & Output Accuracy
Own the observability stack for AI agent behaviour: instrument agents to capture inputs, retrieved context, tool calls, reasoning traces, and outputs — creating a complete audit trail of every agentic action driven by platform data.
Design and operate evaluation frameworks that continuously measure AI output quality: factual accuracy, context faithfulness, retrieval relevance, hallucination rates, and task completion success— across all AI consumers of the platform.
 
Architecture Standards & Engineering Enablement
Define and maintain the reference architecture for the AI data platform — documenting design patterns, data contracts, integration standards, and decision records (ADRs) that all engineering teams follow.
Establish data engineering standards: pipeline testing frameworks, code review practices, CI/CD automation, infrastructure-as-code (Terraform), reusable component libraries, and observability instrumentation.

Benefits

Medical Insurance benefits as per company policy. 

Dental insurance as per company policy.

Vision insurance as per company policy.

Employer paid Disability, Life, and AD&D insurance

Unlimited PTO 

Paid parental leave 

401K

Flexible work policy

12 Paid Holidays