The Challenge
We are moving away from standard batch processing toward complex, low-latency data platforms that serve production-grade ML models and APIs simultaneously. You will face messy legacy data architectures, high enterprise loads, and the need to establish strict data governance, lineage, and observability without degrading system performance. If you are bored with standard CRUD and simple ETL pipelines, this infrastructure challenge will test your engineering limits.
What You'll Be Working On
Architect cloud-native topologies for modern data platforms and reproducible ML pipelines.
Design and implement scalable data pipelines, data models, and event-driven architectures.
Support Machine Learning teams by building robust infrastructure for model training, evaluation, and deployment at scale.
Build secure, flexible, and high-performance REST APIs for internal and external usage.
Implement production-grade observability, data monitoring, and lineage across all systems.
Participate in high-level solution design, technology stack evaluation, and critical architectural reviews.
Must-have
4+ years of proven track record in Data Engineering, Data Platform, or ML Infrastructure roles.
Strong experience building production-grade data pipelines and robust data warehousing (ETL/ELT, data modeling).
Deep expertise with cloud platforms (AWS environment preferred).
Hands-on infrastructure experience with Kafka (or similar streaming tools) and Airflow (or similar orchestrators).
Strong command of SQL and experience with multiple database types (NoSQL, Relational).
Proficiency with Docker, Infrastructure as Code (Terraform / CloudFormation).
English proficiency at a C1 level (excellent written and verbal communication).
Availability to fully align with US business hours (EST to PST timezones).
Nice-to-have
Experience with AWS SageMaker and OpenAI tooling ecosystem.
Familiarity with Graph databases.
Experience with Kubernetes (K8s) container orchestration.
Strong software engineering foundation (Python/Go/Java) applied to data systems.
Why This Role
Real Architectural Ownership: You make choices that directly affect the infrastructure topology.
Direct Access: Zero layers of corporate bureaucracy — you work closely with the engineering leadership and founders.
Niche & High-Growth Domain: Deep immersion into real-world AI agentic infrastructure and LLM production enablement.
