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Accenture

Advises businesses and runs their technology

Large Language Model Architect

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • At least 7.5 years of experience in Snowflake Data Warehouse.
  • At least 5 years of experience as a machine learning engineer or scientist deploying models in production at scale, including monitoring, alerting, automatic bug filing, and auditing.
  • At least 5 years of experience applying theoretical foundations of computer science, including computer system architecture, system engineering, and programming.
  • At least 3 years of experience with distributed computing systems and architecture, which may include big data, high-performance compute, engineering simulations, scientific compute, grid and cloud computing, or distributed networks.
  • At least 2 years of experience building and deploying AI/ML-based software to a cloud environment.
  • Proficiency in Python and Python-based AI/ML frameworks, with familiarity with relevant libraries and frameworks such as TensorFlow and PyTorch.
  • Experience working with language model APIs and optimizing their use for specific applications.
  • Experience with Python, C++, Java, R, and SQL.
  • Strong written and verbal communication skills and the ability to communicate complex technical concepts to non-technical stakeholders.
  • Strong client-facing skills in a consulting environment.
  • Strong cross-functional collaboration skills with internal and client-side teams.
  • A bachelor's degree or equivalent.
  • Fifteen years of full-time education is required.

Responsibilities

  • Design, build, and operationalize artificial intelligence and machine learning solutions for enterprise clients, combining custom models with cloud and third-party AI services to deliver production-ready outcomes.
  • Assess client needs and data, select and customize models, and design scalable data and MLOps/LLMOps pipelines for training and production while ensuring quality, value, and reliability of deployed systems.
  • Formulate real-world problems into practical, efficient, and scalable AI and machine learning solutions.
  • Develop and implement machine learning algorithms, models, and computational systems, and design and build scalable data pipelines to support model training and production with DevOps and MLOps.
  • Customize and apply deep learning and generative AI models for use cases based on business needs, data availability, and system and infrastructure requirements, including edge devices and high-performance computing.
  • Engage in research and development of artificial intelligence and high-performance computing algorithms, models, and simulations, and apply them to complex business problems at client sites.
  • Work with large-scale datasets and apply preprocessing techniques to ensure high-quality inputs for training and production.
  • Implement and maintain efficient data storage and retrieval mechanisms for models and knowledge using appropriate tools.
  • Justify the value of model approaches in business problems.
  • Collaborate with business and technical teams, including users, use-case representatives, business owners, engineers, architects, and UI designers, to achieve end-to-end project goals and integrate solutions into production.
  • Design and structure advanced language models that understand and generate natural language, including architecting neural networks trained on extensive unstructured text datasets.
  • Refine model parameters and collaborate with teams to integrate language models into broader applications.
  • Collaborate with and manage the team to perform, and make decisions for the team.
  • Engage with multiple teams and contribute to key decisions.
  • Provide solutions to problems for the immediate team and across multiple teams.
  • Lead development and implementation of architectural strategies for large language models to meet project goals.
  • Facilitate knowledge sharing and mentorship within the team to enhance technical capabilities.
  • Coordinate cross-functional efforts to align model development with organizational objectives.
  • Design scalable data architectures to support large-scale language model training and deployment.
  • Apply data storage, retrieval, and optimization techniques in cloud-based data warehouse environments.
  • Collaborate with data scientists and engineers to integrate data pipelines with model architectures.
  • Manage and optimize large datasets to enhance model performance and efficiency.

Desired Qualifications

  • Experience in data engineering.
  • Experience with graph databases.
  • A master's or PhD in a related field, such as computer science or engineering.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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