Lead the Design and Evolution of Scalable Data Systems: Architect end-to-end real-time and batch data processing pipelines that power mission-critical applications such as trip intelligence, predictive diagnostics, and geofence-based alerts. Drive system-level design decisions and guide the team through technology tradeoffs.
Compensation
Competitive compensation package with performance incentives.
Responsibilities
Lead the Design and Evolution of Scalable Data Systems
Build robust data ingestion pipelines for Gemini Enterprise for Customer Experience (GECX) and support AI grounding and RAG solutions on Google Cloud Platform.
Locations (2)
Ciudad de Mexico, CDMX, Mexico
Mexico
Compensation
Final compensation offered is based on multiple factors such as the specific role, hiring location, as well as individual skills, experience, and qualifications. In addition to competitive salaries, we offer a comprehensive benefits package.
Responsibilities
Design and maintain automated ETL/ELT data pipelines for structured and unstructured enterprise data
Develop and optimize data pipelines using GCP services such as BigQuery, Cloud Storage, Dataflow, and Cloud Composer
Implement data chunking, tokenization, and embedding strategies for enterprise knowledge retrieval
Integrate vector search solutions with Vertex AI Vector Search and RAG architectures
Collaborate with client data owners to establish data quality, governance, and privacy boundaries
Randstad is hiring a Senior (Lead) Backend Engineer to design, build, and scale high-throughput microservices and data platforms across a hybrid Google Cloud Platform (GCP) and AWS environment.
Compensation
Competitive base salary, variable performance pay, and access to Randstad’s Employee Share Purchase Plan.
Responsibilities
Design and build scalable backend services on GCP with AWS integrations
Own engineering and performance of search engines and vector retrieval workflows powered by AWS OpenSearch
Design resilient, event-driven streaming and batch pipelines using modern DataOps best practices
Partner with data scientists to deploy production-grade MLOps pipelines into live multi-cloud environments
Coach junior and mid-level engineers while shaping technical roadmaps and data governance frameworks
Requirements
Master’s degree in Computer Science, Software Engineering, or equivalent technical experience
The role involves leading the design and implementation of end-to-end MLOps and LLMOps pipelines, developing automation for model deployment, and collaborating with teams to integrate scalable AI systems.
Responsibilities
Lead the design and implementation of end-to-end MLOps and LLMOps pipelines, from model development and training to deployment and continuous monitoring in production environments.
Develop automation for model deployment and management, ensuring scalability, efficiency, and cost-effectiveness, particularly for large language models (LLMs) in cloud environments.
Implement and manage CI/CD pipelines for both ML and LLM models, integrating them seamlessly into the production environment.
Collaborate with data scientists, ML engineers, and software developers to ensure the integration of LLMs and ML models into scalable, production-ready systems.
Build data pipelines and workflows for training and serving models, ensuring efficient data processing and pipeline orchestration using tools like Kubeflow, Airflow, or MLflow.
Lead technical vision and architecture for Cortex Xpanse Collections, designing distributed backend scanning and data pipelines.
Compensation
$183,600.00 - $297,000.00/yr
Responsibilities
Build, optimize, and scale distributed backend scanning systems in Go
Design and develop robust data collection pipelines using Apache Beam and Java multithreading frameworks
Investigate new data sources and perform data analysis to estimate customer impact
Take ownership of projects, drive them to completion, and support them in production
Partner with engineering leadership, product managers, and downstream teams to define product strategy and enable the creation of new customer-facing features
Looking for a Senior Infrastructure Engineer to help build and evolve the systems that products and engineering teams depend on every day, covering cloud infrastructure, deployment, observability, reliability, networking, security, and developer tooling.
Responsibilities
Designing and evolving cloud infrastructure on GCP
Building reliable deployment and CI/CD systems
Improving observability across services, infrastructure, and production workloads
Building tooling that makes engineers faster and reduces operational overhead
Improving reliability, scalability, and resilience across systems
Requirements
Nice to have: Experience supporting data-intensive or machine learning systems
Senior MLOps & Cloud Infrastructure Engineer responsible for building, operating, and evolving the cloud infrastructure and engineering platforms behind production AI systems.
Responsibilities
Design, provision, and operate scalable infrastructure on GCP
Manage containerised applications and ML workloads with Kubernetes
Build reproducible infrastructure using Terraform
Design and improve CI/CD pipelines for backend services and data pipelines
Improve service availability, resilience, and incident response
Looking for a Senior Data Engineer to build reliable data systems, shape how data is collected/used for ML systems, real-time product experiences, and analytics.
Responsibilities
Design and build scalable batch and streaming data pipelines
Build real-time data infrastructure used by production ML inference
Develop feature pipelines and feature serving for machine learning models
Create reliable datasets for analytics, experimentation, and business intelligence
Develop data models and infrastructure that support product and engineering teams
Forward Deployed EngineereComID · Stockholm, Sweden