Remote Java Qa Engineer For Ai Model Training
Outlier
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Job summary
Outlier seeks a Senior Data and AI - Cloud Solution expert to design and deploy Oracle Cloud architectures addressing customer business problems. You will lead data platform design for integration, storage, processing and analytics, and select lake, lakehouse or data warehouse patterns per case.
You will translate architectural decisions into tangible outcomes, balancing security, governance, performance and cost, and help customers move solutions into production with proven OCI/multicloud
Qualifications
- 8+ years of experience in data engineering, data architecture, or related roles.
- Hands‑on experience designing enterprise data platforms that scale with demand.
- Experience with modern analytics architectures and distributed processing.
Responsibilities
- Discover customer data challenges and success criteria.
- Design data platforms for integration, storage, processing, and analytics.
- Recommend data lake, lakehouse, or data warehouse approaches based on customer needs.
- Design batch and real-time pipelines (ETL/ELT) and cross‑platform integration.
- Assess architecture trade-offs for security, governance, performance, scalability, resilience, and cost.
- Ensure reliable operation with monitoring and recovery strategies.
Skills
Education
Tools
Job description
Partners with customers, sales, engineering and product teams to design, demonstrate and deploy Oracle Cloud architectures that address customer business problems. Drives Oracle Cloud customer consumption by accelerating the adoption of Oracle cloud services including discovery, design and deployment.
- Discover customer's data challenges, priorities, growth expectations, and success criteria.
- Design data platforms for integration, storage, processing, and analytics.
- Recommend data lake, lakehouse, or data warehouse approaches based on each customer’s needs and existing environment.
- Design batch and real-time pipelines, including ETL/ELT and integration across platforms.
- Help customers make informed architecture trade-offs across security, data governance, performance, scalability, resilience, operational needs, and cost.
- Design for reliable operation, including monitoring, failure recovery, and the ability to adapt as demand grows.
- Lead technical discussions, demonstrations, and proofs of concept, and help customers bring validated solutions into production.
- Work with customer and internal teams to resolve integration and operational challenges and support long-term adoption.
- Build the data foundation for analytics, machine learning, and generative AI use cases when relevant.
- 8+ years of experience in data engineering, data architecture, or related roles, including hands‑on experience designing enterprise data platforms that scale with demand, recover from failures, and operate reliably in production.
- Practical knowledge of distributed processing, data integration, and modern analytics architectures.
- Experience with technologies such as Spark, Databricks, Snowflake, BigQuery, or cloud data services.
- The ability to connect architecture decisions to business outcomes and explain trade‑offs to different audiences.
- Experience working directly with customers from discovery through delivery.
- Experience with OCI and multicloud architectures is a plus.
If you enjoy understanding what customers need, navigating complex data environments, and helping teams put reliable solutions into production, we’d like to hear from you.
- Comprender los desafíos de datos de cada cliente, sus prioridades de negocio, planes de crecimiento y criterios de éxito.
- Diseñar plataformas para integración, almacenamiento, procesamiento y análisis de datos, eligiendo arquitecturas de data lake, lakehouse o data warehouse según cada caso.
- Diseñar pipelines batch y en tiempo real, incluyendo ETL/ELT e integración entre plataformas.
- Orientar decisiones de arquitectura considerando seguridad, gobierno de datos, rendimiento, escalabilidad, resiliencia, operación y costos.
- Diseñar soluciones preparadas para operar de forma confiable en producción, con monitoreo, recuperación ante fallas y capacidad de adaptación a nuevas demandas.
- Liderar conversaciones técnicas, demostraciones y pruebas de concepto, y ayudar a llevar soluciones validadas a producción.
- Trabajar con equipos del cliente y de Oracle para resolver desafíos de integración y operación e impulsar la adopción sostenida de Oracle Cloud Infrastructure (OCI).
- Establecer la base de datos necesaria para casos de analytics, machine learning e IA generativa cuando corresponda.
- Más de 8 años de experiencia en ingeniería de datos, arquitectura de datos o funciones relacionadas, incluyendo experiencia práctica en el diseño de plataformas empresariales.
- Experiencia con plataformas que escalan, se recuperan de fallas y operan de manera confiable en producción.
- Conocimiento práctico de procesamiento distribuido, integración de datos y arquitecturas modernas de analytics.
- Experiencia con tecnologías como Spark, Databricks, Snowflake, BigQuery o servicios de datos en la nube.
- Capacidad para relacionar decisiones de arquitectura con resultados de negocio y explicar sus ventajas y compromisos a diferentes públicos.
- Experiencia trabajando directamente con clientes, desde el descubrimiento de necesidades hasta la entrega de soluciones.
- Se valorará la experiencia con OCI y arquitecturas multicloud.
Si te interesa comprender las necesidades de los clientes, trabajar en entornos de datos complejos y ayudar a los equipos a llevar soluciones confiables a producción, nos gustaría conocerte.
Mid
Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.
In most instances, this position requires in‑person interviews as part of the hiring process.
Please submit your resume in English - we can only consider applications submitted in this language.
Only applications of candidates with Mexican citizenship will be evaluated for this role in compliance with the provisions of Article 7 of the Federal Labor Law.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Buenos Aires, Argentina; Mexico City, CDMX, Mexico.
- 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.
- 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.
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.
- 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.
Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google’s Applicant and Candidate Privacy Policy (./privacy‑policy).
Google is proud to be an equal opportunity and affirmative action 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. See alsoGoogle’s EEO Policy ( ,Know your rights: workplace discrimination is illegal ( ,Belonging at Google ( , andHow we hire ( .
If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form ( .
Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.
Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also and If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form:
Mid
Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.
In most instances, this position requires in‑person interviews as part of the hiring process.
Please submit your resume in English - we can only consider applications submitted in this language.
Only applications of candidates with Mexican citizenship will be evaluated for this role in compliance with the provisions of Article 7 of the Federal Labor Law.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Bogotá, Bogota, Colombia; Mexico City, CDMX, Mexico; Buenos Aires, Argentina; Santiago, Chile; Lima, Peru.
- Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 5 years of experience with software development using Python or similar coding languages.
- Experience taking production‑grade AI‑driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP).
- Experience building pipelines for structured and unstructured data using both vector databases and RAG‑like architectures to power enterprise AI solutions.
- Experience writing code for machine learning, natural language processing, and generative AI agents.
- Ability to communicate in Spanish and English fluently to support client relationship management in this region.
- Master's degree or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi‑agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self‑reflection, hierarchical delegation).
- Knowledge of "LLM‑native" metrics (e.g., tokens/sec, cost‑per‑request) and techniques for optimizing state management and granular tracing.
As a GenAI Forward Deployed Engineer (FDE) at Google Cloud, you are an embedded builder who bridges the gap between frontier AI products and production‑grade reality within customers. Unlike traditional advisory roles, you will function as an "innovator‑builder," moving beyond high‑level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment. Your role is designed for high‑agency engineers with a founder's mindset. You will address blockers to production including solving the integration complexities, data readiness issues, and state‑management challenges that prevent AI from reaching enterprise‑grade maturity, by embedding with strategic accounts, you serve a dual purpose: providing "white glove" deployment of complex AI systems and acting as a critical feedback loop, transforming real‑world field insights into Google Cloud’s future product roadmap.
It's an exciting time to join Google Cloud's Go‑To‑Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google’s brand credibility-a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world’s most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind’s engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era-the market is yours.
- Serve as a developer for complex AI applications, transitioning from rapid prototypes to production‑grade agentic workflows (e.g., multi‑agent systems, Model Context Protocol (MCP) servers) that drive measurable Return on Investment (ROI).
- Architect and code the "connective tissue" between Google’s AI products and customer’s live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.
- Build high‑performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
- Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
- Collaborate with technical sales teams to instill Google‑grade development best practices, ensuring long‑term project success and high end‑user adoption.
Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google’s Applicant and Candidate Privacy Policy (./privacy‑policy).
Google is proud to be an equal opportunity and affirmative action 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. See alsoGoogle’s EEO Policy ( ,Know your rights: workplace discrimination is illegal ( ,Belonging at Google ( , andHow we hire ( .
If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form ( .
Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.
Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also and If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form:
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