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Accenture

Advises businesses and runs their technology

Large Language Model Architect

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • At least 12 years of experience with Databricks Unified Data Analytics Platform.
  • A Bachelor's degree or equivalent, with 15 years of full-time education required.
  • 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 computer science foundations, 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 computing, engineering simulations, scientific computing, grid and cloud computing, and 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 libraries and frameworks such as TensorFlow and PyTorch.
  • Experience working with language model APIs and optimizing their usage for specific applications.
  • Experience with Python, C++, Java, R, and SQL.
  • Experience designing and optimizing neural network architectures for natural language processing tasks.
  • Understanding of data processing pipelines and handling large-scale unstructured datasets.
  • Familiarity with cloud-based analytics platforms and scalable computing environments.
  • Strong written and verbal communication skills, including explaining complex technical concepts to non-technical stakeholders.
  • Strong client-facing skills in a consulting environment.
  • Ability to collaborate across internal and client-side teams.
  • A Bachelor's degree or equivalent.
  • An MS or PhD in a related field, with computer science and engineering given as examples, is preferred.

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, including deep learning, generative AI, and large language models; design scalable data and MLOps/LLMOps pipelines for training and production; and ensure the 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 and models, 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, 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 use data preprocessing techniques to ensure high-quality input 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 for business problems.
  • Collaborate with users, use case representatives, business owners, engineers, architects, UI designers, and other business and technical stakeholders to achieve end-to-end project goals and integrate solutions into production.
  • Design and structure language models capable of understanding and generating text, including developing neural network configurations and optimizing parameters for large quantities of unstructured text data.
  • Refine model architectures to improve performance and scalability and align models with project goals and real-world applications.
  • Collaborate with and manage the team to perform, and take responsibility for team decisions.
  • Engage with multiple teams and contribute to key decisions.
  • Provide solutions to problems that apply across multiple teams.
  • Lead the development and implementation of architectural strategies for large language models in alignment with organizational objectives.
  • Facilitate knowledge sharing and mentorship within the team.
  • Coordinate cross-functional efforts to integrate language models into broader systems and workflows.
  • Guide junior team members and assist them with problem-solving.

Desired Qualifications

  • Machine learning (ML).
  • Data science.

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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