Senior Gemini Ai Engineer — Remote, Production-Ready
Overseas
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Benefits offered by this job
Job summary
Pearster is hiring a Senior Data and AI Cloud Solution professional to design data platforms, pipelines, and architectures for scalable analytics and AI use cases. This remote role collaborates with customers and internal teams to drive production-ready solutions and successful adoption of OCI and multicloud strategies.
You will lead discussions, design data foundations for analytics, ML, and generative AI, and help customers scale their data workloads across on-demand environments.
Qualifications
- 8+ years of experience in data engineering, data architecture, or related roles with hands-on data platform design.
- Practical knowledge of distributed processing, data integration, and modern analytics architectures.
- Experience with Spark, Databricks, Snowflake, BigQuery or cloud data services.
- Ability to connect architecture decisions to business outcomes and explain trade-offs to different audiences.
- Experience working directly with customers from discovery through delivery.
- OCI and multicloud architectures is a plus.
Responsibilities
- Discover customer data challenges and success criteria to design suitable data platforms.
- Design data pipelines for integration, storage, processing, and analytics.
- Recommend data lake/lakehouse/data warehouse approaches per customer needs.
- Lead technical discussions, proofs of concept, and production deployment.
- Collaborate with customers and internal teams to resolve integration and operational challenges.
Skills
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.
Spoiler: This is where your next big move begins.
At Pearster, we are hiring an AI Engineer to join our team. This is a 100% remote position where you will be responsible for designing, building, and implementing AI-powered solutions that solve real business challenges and create meaningful impact.
You will work with modern Artificial Intelligence technologies, including Generative AI, Large Language Models (LLMs), Machine Learning, and intelligent automation, collaborating with cross-functional teams to transform ideas and business needs into scalable, production-ready solutions.
We are looking for a curious, hands‑on, and innovative professional who enjoys experimenting with emerging technologies, solving complex problems, and turning the latest advances in AI into practical solutions.
Build solutions leveraging Generative AI, Large Language Models (LLMs), and Machine Learning.
Design, develop, and deploy AI‑powered applications and solutions.
Develop and integrate AI agents, APIs, and intelligent automation workflows.
Build solutions using RAG (Retrieval‑Augmented Generation), embeddings, vector databases, and prompt engineering.
Integrate AI models and services from platforms such as OpenAI, Anthropic, Google, AWS, Azure, or similar providers.
Develop prototypes and Proofs of Concept (POCs) and help transform successful experiments into production‑ready solutions.
Evaluate and optimize AI solutions for accuracy, reliability, performance, scalability, and cost.
Collaborate with Engineering, Product, and business stakeholders to identify opportunities where AI can create meaningful value.
Integrate AI capabilities into existing applications, platforms, and workflows.
Contribute to responsible AI practices, including security, privacy, monitoring, and appropriate use of AI systems.
Stay up to date with emerging AI technologies, frameworks, tools, and industry best practices.
Share knowledge and contribute to building AI capabilities and best practices across the organization.
Strong programming skills, preferably in Python.
Professional experience in Software Engineering, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
Hands‑on experience building applications using Generative AI and Large Language Models (LLMs).
Experience integrating APIs and developing backend services.
Knowledge of prompt engineering, RAG, embeddings, vector databases, and AI agents.
Familiarity with AI frameworks and tools such as LangChain, LlamaIndex, Hugging Face, or similar technologies.
Experience working with AI APIs and platforms such as OpenAI, Anthropic, Google Gemini, or similar.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
Understanding of Git, APIs, CI/CD, and modern software development practices.
Strong analytical and problem‑solving skills.
Ability to work independently in a fully remote environment.
Strong communication and collaboration skills.
Intermediate to advanced English level to collaborate effectively in an international environment.
We’re here to amplify your brilliance, not contain it.
with true flexibility and freedom.
with compensation that matches your expertise.
with dedicated paid time off.
with fully covered international certifications.
whenever you want a professional setup.
and expand your global reach.
with activities that unite our international team.
with personalized gifts and a thoughtful welcome kit.
and earn through our referral program.
At Pearster, your journey matters, and we’re here to help you go further than you imagined.
LangChain.
LangGraph.
AWS.
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
We are looking for a Lead AI Engineer to design and lead GenAI and agentic workflows that automate cloud security policies and remediation.
The mandatory requirements are AI architecture and strategy experience, 6+ years in software engineering on public cloud, 2+ years building agentic AI with RAG and vector databases, technical leadership, and an upper‑intermediate English level.
AI Architecture & Strategy: architect Generative AI workflows (utilizing RAG, vector databases, and LLM APIs) to automate the creation of configuration requirements, Rego policies, and Terraform modules for new cloud services (e.g., AWS S3).
Cloud Security Automation: oversee the development of AI‑enabled drift detection, continuous discovery, and gap analysis capabilities to accelerate response and remediation activities.
Policy & Infrastructure as Code (IaC): accelerate the generation and validation of compliant Terraform modules, Rego policies (for Wiz Custom Configuration Rules), and Regula test cases for CI/CD pipelines.
Technical Leadership: act as the technical lead for the Cloud Security Tooling team’s AI enablement initiatives, designing service architecture, and driving extensible frameworks.
6+ years of software engineering experience developing applications and services hosted on public cloud infrastructure.
2+ years of experience with cloud infrastructure deployment pipelines and infrastructure as code (IaC).
2+ years of experience developing agentic models (e.g. OpenAI, Anthropic) using Retrieval‑Augmented Generation (RAG) and vector databases.
Proven capability in technical leadership in a global organization, developing technology strategy and driving technology programs.
Proven capability to lead and mentor engineers in a distributed, asynchronous, follow-the-sun model.
Hands‑on experience with modern AI orchestration frameworks (LangChain, LangGraph).
Hands‑on experience writing Rego policies (Open Policy Agent) or Regula test cases.
Hands‑on experience with Terraform.
Familiarity with cloud-native security services in Amazon Web Services, Azure, and Google Cloud Platform.
Familiarity with PagerDuty, Atlassian Suite, and ServiceNow.
Professional growth : Mentorship, TechTalks, and personalized growth roadmaps.
Competitive compensation : USD‑based pay with education, fitness, and team activity budgets.
Exciting projects : Modern solutions with Fortune 500 and top product companies.
Flextime : Flexible schedule with remote and office options.
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
We are looking for a Senior AI Engineer to design and build agentic workflows and AI‑powered products for enterprise customers in higher education.
5+ years of experience in a software product development environment.
Strong proficiency with Java and Spring Boot.
Experience with relational databases.
Hands‑on experience building AI agents, tool‑using LLM applications, or orchestrated workflows.
Experience using agents to support coding, code review, testing, release management, or incident response.
Senior‑level experience building and operating production software systems.
Experience with TypeScript, Python, RESTful APIs, and infrastructure on AWS.
Experience with APIs, workflow engines, event‑driven architectures, integrations, or distributed systems.
Strong understanding of testing, observability, security, permissions, and failure recovery.
Ability to turn ambiguous opportunities into practical, reliable technical solutions.
Strong communication skills and the ability to influence engineering practices across a team.
Experience building enterprise SaaS or multi‑tenant systems.
Leadership mindset and ability to drive changes across different teams.
Experience with multi‑agent systems, RAG, vector search, agent evaluation, or human‑in‑the‑loop workflows.
Familiarity with higher education technology and its privacy and compliance requirements.
Experience leading technical transformation or introducing new engineering practices.
Design and build agentic workflows that coordinate tools, services, data, and human approvals.
Develop reusable patterns for agent orchestration, tool use, context management, memory, and multi‑agent collaboration.
Use agentic coding tools throughout the development lifecycle, including planning, implementation, testing, debugging, refactoring, code review, and documentation.
Integrate agents into software development, testing, deployment, support, and operational workflows.
Establish standards for agent evaluation, observability, reliability, security, and governance.
Prototype new agentic capabilities and turn successful experiments into production systems.
Build complete, usable products—not just demonstrations or isolated model integrations.
Identify opportunities to improve engineering productivity and product delivery through agentic development.
Mentor engineers and help shape an agentic‑first technical strategy across the organization.
Collaborate with product and engineering leaders to connect agentic capabilities to meaningful outcomes.
Demonstrate the ability to use AI coding agents as productive engineering collaborators across an application codebase.
Provide agents with clear context, constraints, architecture, acceptance criteria, and technical direction.
Review, test, secure, and improve agent‑generated code rather than accepting outputs without verification.
Decompose complex application work into agent‑ready tasks while maintaining architectural consistency and product intent.
Create workflows in which agents can reason, select tools, call APIs, manage state, recover from failures, and escalate to people when appropriate.
Design agentic applications around real user needs, including intuitive experiences, reliable workflows, permissions, audit trails, and meaningful error handling.
Move from prototype to production by addressing performance, scalability, security, monitoring, evaluation, cost, and ongoing maintenance.
Understand problems deeply, make appropriate tradeoffs, and deliver complete solutions that customers can depend on.
Professional growth : Mentorship, TechTalks, and personalized growth roadmaps.
Competitive compensation : USD‑based pay with education, fitness, and team activity budgets.
Exciting projects : Modern solutions with Fortune 500 and top product companies.
Flextime : Flexible schedule with remote and office options.
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
In most instances, this position requires in‑person interviews as part of the hiring process.
Let us 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.
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 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 it’s 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.
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. 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. ...
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