Remote Aws Cloud Engineer For Ai Code Qa & Training

Outlier; Quilmes; A distancia; ARS 138.361.000 - 199.855.000; Jornada completa

Descripción de la vacante

Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google's global network, web-scale data centers, and software infrastructure.

As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.

Formación

• Bachelor's degree in Computer Science or equivalent practical experience.
• 3 years of experience building machine learning solutions and working with technical customers.
• Experience designing cloud enterprise solutions and supporting customer projects to completion.
• Experience building and deploying custom ML models into production, including deep learning frameworks and agentic AI workflows for business process automation.
• Experience coding in Python, including data structures, algorithms, and software design.
• Ability to communicate in English fluently as this is a customer-facing role.

Responsabilidades

• Be a trusted technical advisor to customers and solve complex machine learning challenges.
• Coach customers on practical challenges in machine learning systems: feature extraction, data validation, monitoring, and management of features and models.
• Work with Customers, Partners, and Google Product teams to deliver tailored solutions into production.
• Create and deliver best practice recommendations, tutorials, blog articles, and sample code.
• Travel up to 30% in-region for meetings, technical reviews, and onsite delivery activities.

Conocimientos

AWS EKS

Terraform

Jenkins

Spinnaker

Docker

AWS Lambda

AWS Neptune

AWS Glue

SQL

NoSQL ElasticSearch

Agile

Datadog

CloudWatch

Dynatrace

Educación

Bachelor's degree in Computer Science or related field

Herramientas

S3

Karpenter

Kustomize

Istio

Descripción del empleo

We are seeking an experienced Infrastructure engineer to join our Market Data Services team, focusing on our high-impact Market Data Distribution program .

As a Lead Engineer at JPMorgan Chase within our Market Data Services team, you will play a pivotal role in delivering third party data, via cloud-based Data Platforms for business use across JPMorgan including AI/ML. You will be working within an Agile project environment, participating in two-week sprint cycles, and collaborating closely with cross-functional teams to deliver high-quality solutions.

Key Responsibilities

• Building and maintaining AWS EKS Environments
• Configure monitoring for cloud environments
• Extract Transform Load data from Third Parties into AWS Databases
• Collaborate with product owners and stakeholders to ensure data solutions align with business and regulatory requirements.
• Manage the risk profile of Market Data applications in line with Cyber Security Control Procedures

Actively participate in Agile ceremonies and two-week sprint cycles.

Required qualifications, skills, and capabilities

• Strong hands‑on expertise with AWS EKS and Terraform for building and maintaining clusters
• Experience with build pipelines Jenkins and Spinnaker
• Deep familiarity with Docker containerizing applications
• Experience with AWS Lambda, AWS Neptune, AWS Glue.
• Proficient in SQL for Oracle and No-SQL DSL for ElasticSearch.
• Experience working in Agile teams.
• Excellent communication and stakeholder management skills.
• Datadog, Cloudwatch or Dynatrace experience

Preferred qualifications, skills, and capabilities

Any experience with S3, Karpenter, Kustomize and Istio will be favorable.

Cloud AI Engineer, Google Cloud (English)
Share Cloud AI Engineer, Google Cloud (English)
_corporate_fare_ Google _place_ Buenos Aires, Argentina; Mexico City, CDMX, Mexico
**Mid**
Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.
Share Cloud AI Engineer, Google Cloud (English)
_info_outline_
XIn most instances, this position requires in-person interviews as part of the hiring process.
**Minimum qualifications:**
+ Bachelor's degree in Computer Science or equivalent practical experience.
+ 3 years of experience building machine learning solutions and working with technical customers.
+ Experience designing cloud enterprise solutions and supporting customer projects to completion.
+ Experience building and deploying custom ML models into production, including experience with deep learning frameworks and with designing and deploying agentic AI workflows for business process automation.
+ Experience coding in Python, including data structures, algorithms, and software design.
+ Ability to communicate in English fluently as this is a customer-facing role.
**Preferred qualifications:**
+ Experience working with recommendation engines, data pipelines, or distributed machine learning.
+ Experience with deep learning frameworks (e.g., TensorFlow, PyTorch, XGBoost).
+ Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments (e.g., Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume).
+ Understanding of the auxiliary practical concerns in production machine learning systems.
**About the job**
The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google's global network, web‑scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.
As a Cloud AI Engineer, you will design and implement machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and Vertex AI. You will work with customers to identify opportunities to apply machine learning in their business, and travel to customer sites to deploy solutions and deliver workshops to educate and empower customers. Additionally, you will work closely with Product Management and Product Engineering to build and constantly drive excellence in our products.
In this role, you will work with key Google Cloud customers. Together with the team you will support customer implementation of Google Cloud products through: architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, and much more.
Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise‑grade solutions that leverage Google's cutting‑edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
**Responsibilities**
+ Be a trusted technical advisor to customers and solve complex machine learning challenges.
+ Coach customers on the practical challenges in machine learning systems: feature extraction and feature definition, data validation, monitoring, and management of features and models.
+ Work with Customers, Partners, and Google Product teams to deliver tailored solutions into production.
+ Create and deliver best practice recommendations, tutorials, blog articles, and sample code.
+ Travel up to 30% in‑region for meetings, technical reviews, and onsite delivery activities.
Google is proud to be an equal opportunity employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents‑to‑be, criminal histories consistent with legal requirements, or any other basis protected by law.