Accenture
Google Cloud GenAI Developer
Accenture, San Diego, California, United States, 92189
Are you ready to step up to the New and take your technology expertise to the next level?
Join Accenture and help transform leading organizations and communities around the world. The sheer scale of our capabilities and client engagements and the way we collaborate, operate and deliver value provides an unparalleled opportunity to grow and advance. Choose Accenture and make delivering innovative work part of your extraordinary career.
People in our Client Delivery & Operations career track drive delivery and capability excellence through the design, development and/or delivery of a solution, service, capability or offering. They grow into delivery-focused roles, and can progress within their current role, laterally or upward.
As part of our Google Cloud Platform practice, you will lead technology innovation for our clients through robust delivery of world-class solutions. There will never be a typical day and that’s why people love it here. The opportunities to make a difference within exciting client initiatives are unlimited in the ever-changing technology landscape. You will be part of a growing network of technology experts who are highly collaborative taking on today’s biggest, most complex business challenges. We will nurture your talent in an inclusive culture that values diversity. Come grow your career in technology at Accenture!
Generative AI Developer
The Generative AI Developer is responsible for designing, implementing, and managing cutting-edge Generative AI solutions on the Google Cloud Platform (GCP). This individual will be a technical leader, collaborating with various teams to understand client needs and translate them into robust Generative AI solutions. The ideal candidate possesses a deep understanding of Generative AI models, MLOps principles, and GCP's AI/ML services. We are looking for candidates who have a broad set of technology skills and who can demonstrate an ability to design the right solutions with appropriate combinations of GCP and 3rd party technologies for deploying on the GCP cloud. Key Responsibilities
Develop and implement GenAI solutions, collaborating with cross-functional teams, and supporting the successful execution of AI projects for a diverse range of clients. Experience developing and maintaining ML systems built with open source tools. Conduct model tuning and optimization to improve model accuracy, efficiency, and robustness. Fluency in Python. Demonstrate deep knowledge of ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn. Develop and optimize search models, pipelines, and workflows for efficient data retrieval and relevance ranking. Utilize Google Vertex AI AutoML capabilities to build custom search models for specific use cases. Integrate VertexAI search functionalities into existing applications and systems, ensuring seamless user experiences. Collaborate with data engineers, software developers, and business stakeholders to understand search requirements and deliver solutions accordingly. Implement best practices for data indexing, query optimization, and performance tuning within the Google Vertex AI framework. Performance Optimization: Monitor, analyze, and optimize data platform performance to ensure optimal efficiency and cost-effectiveness. Technology Evaluation: Stay updated on the latest GCP data technologies, evaluating and recommending their adoption within the organization. Collaboration: Work collaboratively with data engineers, data scientists, business analysts, and other stakeholders to understand requirements and deliver optimal solutions. Documentation: Develop clear and comprehensive documentation, including architectural diagrams, design specifications, and operational guidelines. Travel may be required for this role. The amount of travel will vary from 25% to 100% depending on business need and client requirements. Basic Qualifications
Minimum 3 years of experience designing and deploying with one or more from the following ML frameworks: TensorFlow, PyTorch, JAX, Spark ML, etc. Minimum 3 years of experience training and fine-tuning models in large-scale environments (e.g., image, language, recommendation) with accelerators. Minimum 2 years experience with distributed training and optimizing performance versus costs. Minimum 2 years of experience with CI/CD solutions in the context of MLOps and LLMOps including automation with IaC (e.g., using terraform). Minimum 2 years experience in systems design with the ability to design and explain data pipelines, ML pipelines, and ML training and serving approaches. Minimum 3 years of experience working with RAG technologies and LLM frameworks, LLM model registries (VertexAI Model Garden, Hugging Face), LLM APIs, embedding models, and vector databases. Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have a minimum 6 years work experience) Preferred Qualifications
Experience participating in projects that focused on one or more of the following areas: Predictive Analytics, Data Design, Generative AI, Machine Learning, ML Ops. Generative AI Studio: Experience with Generative AI Studio for prototyping and experimenting with generative AI models. Model Garden: Familiarity with Google's Model Garden and its offerings for accessing and deploying pre-trained GenAI models. MLOps for GenAI: Experience in implementing MLOps practices for the development, deployment, and monitoring of GenAI models. Cloud Architecture: Proven track record in designing and implementing cloud-based data architectures. Problem-Solving: Excellent analytical and problem-solving skills. Communication: Strong communication and interpersonal skills, capable of collaborating effectively with various teams. Certifications: GCP Machine Learning Engineer or equivalent certifications are highly desirable.
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The Generative AI Developer is responsible for designing, implementing, and managing cutting-edge Generative AI solutions on the Google Cloud Platform (GCP). This individual will be a technical leader, collaborating with various teams to understand client needs and translate them into robust Generative AI solutions. The ideal candidate possesses a deep understanding of Generative AI models, MLOps principles, and GCP's AI/ML services. We are looking for candidates who have a broad set of technology skills and who can demonstrate an ability to design the right solutions with appropriate combinations of GCP and 3rd party technologies for deploying on the GCP cloud. Key Responsibilities
Develop and implement GenAI solutions, collaborating with cross-functional teams, and supporting the successful execution of AI projects for a diverse range of clients. Experience developing and maintaining ML systems built with open source tools. Conduct model tuning and optimization to improve model accuracy, efficiency, and robustness. Fluency in Python. Demonstrate deep knowledge of ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn. Develop and optimize search models, pipelines, and workflows for efficient data retrieval and relevance ranking. Utilize Google Vertex AI AutoML capabilities to build custom search models for specific use cases. Integrate VertexAI search functionalities into existing applications and systems, ensuring seamless user experiences. Collaborate with data engineers, software developers, and business stakeholders to understand search requirements and deliver solutions accordingly. Implement best practices for data indexing, query optimization, and performance tuning within the Google Vertex AI framework. Performance Optimization: Monitor, analyze, and optimize data platform performance to ensure optimal efficiency and cost-effectiveness. Technology Evaluation: Stay updated on the latest GCP data technologies, evaluating and recommending their adoption within the organization. Collaboration: Work collaboratively with data engineers, data scientists, business analysts, and other stakeholders to understand requirements and deliver optimal solutions. Documentation: Develop clear and comprehensive documentation, including architectural diagrams, design specifications, and operational guidelines. Travel may be required for this role. The amount of travel will vary from 25% to 100% depending on business need and client requirements. Basic Qualifications
Minimum 3 years of experience designing and deploying with one or more from the following ML frameworks: TensorFlow, PyTorch, JAX, Spark ML, etc. Minimum 3 years of experience training and fine-tuning models in large-scale environments (e.g., image, language, recommendation) with accelerators. Minimum 2 years experience with distributed training and optimizing performance versus costs. Minimum 2 years of experience with CI/CD solutions in the context of MLOps and LLMOps including automation with IaC (e.g., using terraform). Minimum 2 years experience in systems design with the ability to design and explain data pipelines, ML pipelines, and ML training and serving approaches. Minimum 3 years of experience working with RAG technologies and LLM frameworks, LLM model registries (VertexAI Model Garden, Hugging Face), LLM APIs, embedding models, and vector databases. Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have a minimum 6 years work experience) Preferred Qualifications
Experience participating in projects that focused on one or more of the following areas: Predictive Analytics, Data Design, Generative AI, Machine Learning, ML Ops. Generative AI Studio: Experience with Generative AI Studio for prototyping and experimenting with generative AI models. Model Garden: Familiarity with Google's Model Garden and its offerings for accessing and deploying pre-trained GenAI models. MLOps for GenAI: Experience in implementing MLOps practices for the development, deployment, and monitoring of GenAI models. Cloud Architecture: Proven track record in designing and implementing cloud-based data architectures. Problem-Solving: Excellent analytical and problem-solving skills. Communication: Strong communication and interpersonal skills, capable of collaborating effectively with various teams. Certifications: GCP Machine Learning Engineer or equivalent certifications are highly desirable.
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