Data Architect

Pyramid Consulting, Inc; Mexico; On-site; MXN 600,000 - 900,000; Full time

Job summary

Pyramid Consulting, Inc. seeks experienced technologists for advanced AI/ML and data platform initiatives. You will design AWS-based data architectures, implement Python-driven data pipelines, and integrate AI components across edge and cloud environments.

Ideal candidates bring manufacturing/engineering domain exposure, strong DevOps, and hands-on leadership to deliver secure, scalable, and governance-aligned solutions.

Qualifications

• Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• Experience in manufacturing, automotive, engineering, industrial, or product-development environments.
• Experience with AWS services such as S3, RDS, Redshift, Glue, Lambda, API Gateway, SageMaker, Bedrock, IAM, CloudWatch, and EKS.
• Experience with knowledge graphs, enterprise search, vector databases, or semantic data models.
• Experience with MLOps platforms and model lifecycle management.
• Experience deploying AI or computer vision solutions at the edge.
• Familiarity with data-governance frameworks, model-risk management, and responsible AI principles.
• AWS, data architecture, security, Kubernetes, or AI/ML certifications.
• Experience leading proof-of-concept efforts and transitioning them into supported production solutions.
• Enterprise and systems thinking
• Hands-on technical leadership
• Data and AI governance
• Security-by-design
• Problem-solving and root-cause analysis
• Stakeholder alignment
• Technical documentation
• Project prioritization and roadmap development
• Ability to balance innovation, security, scalability, cost, and operational sustainability

Skills

AWS

Python

SQL

APIs

Relational DB

NoSQL

Data lakes

Data warehouses

Vector databases

ETL/ELT

Event streaming

Messaging

Microservices

GenAI

AI agents

RAG

Prompt orchestration

Embeddings

Model evaluation

Responsible AI

OpenCV

Object detection

OCR

CI/CD

IaC

Docker

Kubernetes

Git

Secure coding

Vulnerability scanning

Secrets management

IAM

Encryption

Audit logging

Compliance

Monitoring

Observability

Edge AI

Education

Master's degree

Tools

AWS

Docker

Kubernetes

Job description

• Cloud: AWS, including services for compute, storage, databases, security, monitoring, and AI/ML
• Programming: Python, SQL, scripting, and API development
• Data Platforms: Relational databases, NoSQL, data lakes, data warehouses, and vector databases
• Data Integration: ETL/ELT, REST APIs, event streaming, messaging, and microservices
• AI: Generative AI, AI agents, RAG, prompt orchestration, embeddings, model evaluation, and responsible AI controls
• Computer Vision: OpenCV, image preprocessing, classification, object detection, OCR, model evaluation, and inference pipelines
• DevOps: Git, automated testing, CI/CD, infrastructure as code, release management, and environment management
• Containers: Docker and Kubernetes or equivalent container platforms
• Security: Secure coding, vulnerability scanning, secrets management, IAM, encryption, audit logging, and compliance
• Monitoring: Application, database, pipeline, infrastructure, and AI-model observability

Preferred Qualifications

• Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• Experience in manufacturing, automotive, engineering, industrial, or product-development environments.
• Experience with AWS services such as S3, RDS, Redshift, Glue, Lambda, API Gateway, SageMaker, Bedrock, IAM, CloudWatch, and EKS.
• Experience with knowledge graphs, enterprise search, vector databases, or semantic data models.
• Experience with MLOps platforms and model lifecycle management.
• Experience deploying AI or computer vision solutions at the edge.
• Familiarity with data-governance frameworks, model-risk management, and responsible AI principles.
• AWS, data architecture, security, Kubernetes, or AI/ML certifications.
• Experience leading proof-of-concept efforts and transitioning them into supported production solutions.
• Enterprise and systems thinking
• Hands-on technical leadership
• Data and AI governance
• Security-by-design
• Problem-solving and root-cause analysis
• Stakeholder alignment
• Technical documentation
• Project prioritization and roadmap development
• Ability to balance innovation, security, scalability, cost, and operational sustainability