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Senior ML Engineers - RAG/Vertex AI Solutions are responsible for designing, developing, and deploying cutting-edge machine learning solutions leveraging Google Cloud's Vertex AI platform, with a focus on Retrieval Augmented Generation (RAG) models and agent frameworks. This individual will be a technical leader, collaborating with various teams to translate complex business needs into robust and scalable AI solutions. We are looking for candidates who have a proven track record in building and deploying GenAI solutions and possess a deep understanding of machine learning frameworks and cloud technologies.
Key Responsibilities may include:
Design, develop, and deploy machine learning models using Vertex AI to solve complex problems.
Work on RAG models and Agent Frameworks to enhance GenAI solutions by incorporating relevant information retrieval mechanisms and frameworks.
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 depending on business need and client requirements.
Basic Qualifications:
Minimum 5 years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Minimum 5 years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
GCP Expertise: Minimum 3 years extensive experience with GCP data services, including BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, and related technologies.
Minimum 2 years experience architecting high-impact GenAI solutions for diverse clients.
Minimum 2 years experience working with RAG technologies and LLM frameworks, LLM model registries (VertexAI Model Garden, Hugging Face), LLM APIs, embedding models, and vector databases.
Minimum 2 years experience participating in projects that focused on one or more of the following areas: Predictive Analytics, Data Design, Generative AI, Machine Learning, ML Ops.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate's Degree, must have a minimum 6 years work experience).
Preferred Qualifications:
GCP 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.
Experience as a mentor, tech lead or leading an engineering team
Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired in California, Colorado, District of Columbia, Maryland, New York or Washington as set forth below.We accept applications on an on-going basis and there is no fixed deadline to apply.
Information on benefits is here. (https://www.accenture.com/us-en/careers/local/total-rewards)
Role Location Annual Salary Range
California $73,000 to $192,600
Colorado $73,000 to $166,400
District of Columbia $77,700 to $177,200
New York $67,600 to $192,600
Maryland $67,600 to $154,100
Washington $77,700 to $177,200
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