AI Solutions Deployment Lead
Role details
Job location
Tech stack
Job description
As a AI Solutions Deployment Lead for all EMEA and APAC deployments, you are a high-visibility team multiplier responsible for turning the promise of agentic AI into production reality for Google's most strategic global customers. You sit at the nexus of product, engineering, and the customer, translating complex business friction into elegant, reasoning-based AI solutions.
In this leadership role, you will serve as the primary Directly Responsible Individual (DRI) for delivery of agentic solutions, leading a group of deployment specialists to implement best practices for delivering at scale and velocity. You will maintain a pulse on the entire portfolio, ensuring you know exactly where the team stands regarding every deployment at any given point. You are a builder, a strategic systems thinker, and a master of navigating complex technical and business challenges. You will be leading a group of deployment specialists while simultaneously incubating new products and defining the best practices that will enable the entire Google Cloud ecosystem.
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
Serve as the primary DRI and central authority for the deployment portfolio across EMEA and APAC, maintaining a real-time pulse on project health for executive leadership.
Manage and mentor a group of deployment specialists, implementing standardized delivery frameworks to ensure high-fidelity, production-grade leadership across industry verticals.
Advocate foundational architectural frameworks such as agent and pioneer advanced techniques like prompting and cyclic reasoning loops to accelerate time-to-value.
Design and implement evaluation pipelines, utilizing gold datasets and automated judge Large Language Model (LLM) frameworks to benchmark latency, accuracy, and brand fidelity.
Author and scale technical assets, including architectural patterns, prompt engineering guides, and reusable internal tools to transform bespoke solutions into repeatable global motions.
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 also Google's EEO Policy, Know your rights: workplace discrimination is illegal, Belonging at Google, and How we hire.
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., A leading technology company is seeking an experienced AI Solutions Deployment Lead to oversee AI solutions deployment across EMEA and APAC. This strategic role involves managing deployment specialists, designing evaluation pipelines for Large Language Models, and ensuring...
Requirements
Bachelor's degree or equivalent practical experience.
10 years of experience in customer-facing technical roles (e.g., solutions architect, forward deployed engineer, or principal consultant).
Experience in one or more discipline areas such as machine learning, recommendation systems, natural language processing, pattern recognition, or artificial intelligence.
Experience in Large Language Models (LLMs), prompt engineering, and conversational AI frameworks.
Preferred qualifications
Experience in a statistical programming language like R or Python and applied machine learning techniques.
Experience with conversational AI frameworks (e.g., langchain, agent development kit) and enterprise contact center technologies (e.g., genesys, twilio).
Experience with multi-agent architectures and the nuances of cyclic reasoning loops.
Proficiency in a major programming language (e.g., Python, Java, or Go).
Ability to bridge the gap between LLM capabilities and deterministic enterprise systems (e.g., Salesforce, SAP).
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