Senior ML Engineer - RAG, VertexAI Solutions

$73,000 - $192,600/Yr

Accenture - Boston, MA

Full-time - Senior

Boston, MA

Professional, Scientific, and Technical Services

About the position

The Senior ML Engineer - RAG/Vertex AI Solutions at Accenture is responsible for designing, developing, and deploying advanced machine learning solutions using Google Cloud's Vertex AI platform. This role focuses on Retrieval Augmented Generation (RAG) models and agent frameworks, requiring a technical leader who can translate complex business needs into scalable AI solutions. The position offers opportunities for innovation and collaboration within a diverse team, contributing to impactful client initiatives in a dynamic technology landscape.

Responsibilities

• 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.
• Monitor, analyze, and optimize data platform performance to ensure optimal efficiency and cost-effectiveness.
• Stay updated on the latest GCP data technologies, evaluating and recommending their adoption within the organization.
• Develop clear and comprehensive documentation, including architectural diagrams, design specifications, and operational guidelines.

Requirements

• Minimum 5 years of full software development life cycle experience, including coding standards, code reviews, source control management, build processes, testing, and operations.
• Minimum 5 years of leading design or architecture of new and existing systems experience.
• 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, LLM APIs, embedding models, and vector databases.
• Minimum 2 years experience participating in projects focused on Predictive Analytics, Data Design, Generative AI, Machine Learning, ML Ops.
• Bachelor's degree or equivalent work experience.

Nice-to-haves

• Experience with Generative AI Studio for prototyping and experimenting with generative AI models.
• Familiarity with Google's Model Garden and its offerings for accessing and deploying pre-trained GenAI models.
• Experience in implementing MLOps practices for the development, deployment, and monitoring of GenAI models.
• Proven track record in designing and implementing cloud-based data architectures.
• Excellent analytical and problem-solving skills.
• Strong communication and interpersonal skills, capable of collaborating effectively with various teams.
• GCP Machine Learning Engineer or equivalent certifications are highly desirable.
• Experience as a mentor, tech lead or leading an engineering team.

Benefits

• Competitive salary based on location and experience.
• Diversity and inclusion initiatives.
• Professional development opportunities.
• Flexible work arrangements.
• Health and wellness programs.