Description:

Tiger Analytics is seeking a highly skilled and visionary Generative AI Architect to lead the design, development, and implementation of generative AI solutions on Google Cloud Platform (GCP).
The role is part of the Data Engineering team and involves leveraging generative models to drive innovation and solve complex business challenges.
The architect will work closely with cross-functional teams, including data scientists, machine learning engineers, product managers, and business stakeholders.
Responsibilities include defining the end-to-end architecture for generative AI applications, developing a GenAI strategy, evaluating generative AI models, designing data pipelines, and establishing best practices for GenAI solutions on GCP.
The architect must demonstrate deep expertise in GCP AI/ML services and lead the implementation of GenAI solutions, providing technical guidance to engineering teams.
Additional responsibilities include designing infrastructure-as-code for GenAI deployments, optimizing model performance, and ensuring integration with existing enterprise systems.

Requirements:

Candidates must have 12+ years of experience in designing and implementing large-scale AI/ML solutions.
A minimum of 5+ years of hands-on experience with Google Cloud Platform (GCP) AI/ML services is required.
A deep understanding of generative AI models, including large language models, diffusion models, and GANs, is essential.
Strong experience with data engineering pipelines and tools on GCP, such as Dataflow, Dataproc, and BigQuery, is necessary.
Proficiency in programming languages relevant to AI/ML, particularly Python, is required.
Candidates should be able to work independently and collaboratively in a fast-paced environment.
Experience with deploying GenAI models for specific use cases, such as content generation and synthetic data generation, is needed.
Knowledge of responsible AI frameworks and ethical considerations in AI development is important.
Familiarity with MLOps practices and tools for managing ML models on GCP is required.
Experience with infrastructure-as-code tools like Terraform or Cloud Deployment Manager on GCP is necessary.
A solid understanding of cloud security principles and best practices on GCP is essential.
Candidates must possess excellent communication, presentation, and interpersonal skills.

The position offers significant career development opportunities as the company grows.
It provides a unique chance to be part of a small, fast-growing, challenging, and entrepreneurial environment.
The role includes a high degree of individual responsibility, allowing for impactful contributions to the organization.