Job Description

Job Description
You will lead technical execution in cloud-native environments (GCP/AWS) and create prototypes that solve real problems. You will be responsible for the technical delivery of innovation-focused initiatives, from concept to implementation.
You will collaborate with cross-functional teams to identify opportunities and shape solutions. You will design, build, and deploy AI-based solutions using services such as Vertex AI, LangChain, and LLM APIs. You will integrate machine learning models into internal tools, workflows and user-facing products.
Responsibilities:
Create prototypes and iterate quickly on new applications or internal tools to validate ideas.
Develop agile engineering workflows, orchestration patterns, and scalable AI solutions.
Design cloud-native solutions in GCP and AWS, applying best practices for scalability, security and automation.
Implement infrastructure as code using tools such as Terraform or CloudFormation.
Support automation, API integration, and serverless deployments across multiple environments.
Contribute to a wide range of engineering activities, including scripting, automation, cloud configuration and backend development.
Resolve ambiguous or undefined technical problems with practical and creative solutions.
Provide support for troubleshooting, debugging, and optimizing solutions after implementation.
Evaluate emerging technologies and frameworks and recommend potential applications to support internal and external initiatives.
Document and share findings with internal teams to drive knowledge sharing and reuse.
Requirements:
Competition with Google Cloud Platform (GCP) and Amazon Web Services (AWS), including services such as Vertex AI, Cloud Functions, BigQuery, Cloud Run, Pub/Sub, Lambda, DynamoDB, and S3.
Experience using infrastructure-as-code tools, particularly Terraform or CloudFormation, to create scalable and secure environments.
Ability to develop and integrate APIs, particularly RESTful services, to support both internal tools and external applications.
Proficiency in Python for scripting, prototyping and AI/ML workflows, familiarity with JavaScript or Java is an advantage.
Experience working with AI/ML platforms and frameworks such as Vertex AI, LangChain, and large language model APIs (OpenAI, Gemini).
Comfortable using CI/CD pipelines (GitHub Actions, Cloud Build, or Jenkins) to automate deployment and streamline development workflows.
Knowledge of cloud networking and security, including IAM roles, service accounts, VPC, API authentication, and encryption best practices.
Familiarity with containerization tools such as Docker and working knowledge of serverless and event-driven architectures.
Experience with collaboration and project tools such as Jira, Confluence, Slack, and Google Workspace for cross-team coordination.
Ability to quickly evaluate new technologies, create proofs of concept, and contribute to technical decisions in an experimental environment.

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