Enterprise Architect/Data Architect

Job description :

Key Responsibilities (The "Strong Wants")

AI/ML Strategy: Design the architectural framework for scaling Artificial Intelligence and Machine Learning models. This includes building pipelines for LLMs, Generative AI, and predictive analytics.

AWS Cloud Governance: Act as the lead architect for AWS environments, ensuring best practices in VPC design, serverless architectures (Lambda), and cost optimization (FinOps).

Data Mesh & Analytics: Overhaul legacy data silos into a modern Data Lakehouse or Data Mesh architecture to support real-time business intelligence and data-driven decision-making.

AI Ethics & Security: Establish guardrails for data privacy in AI models and ensure AWS security protocols (IAM, GuardDuty) are strictly followed.

Preferred Technical Stack :

Cloud Platform: Proficiency in AWS Ecosystem (S3, SageMaker, Redshift, Glue, Bedrock, and EKS).

Data Frameworks: Experience with Snowflake, Databricks, or Apache Spark for large-scale data processing.

AI Frameworks: Familiarity with PyTorch, TensorFlow, or LangChain for integrating AI into enterprise workflows.

Automation: Strong background in IaC (Infrastructure as Code) using Terraform or AWS CloudFormation.

Skills & Qualifications

• AWS Certifications: Highly preferred (e.g., AWS Certified Solutions Architect – Professional or AWS Certified Data Engineer).

• Analytics Background: Proven track record of designing platforms that handle Petabyte-scale data and complex ETL/ELT processes.

• AI Integration: Experience moving AI projects from "Proof of Concept" (PoC) to full-scale enterprise production.