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Accenture

Advises businesses and runs their technology

Large Language Model Architect

Natural Language Processing & LLMsUpdated on Nov 11
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • A bachelor's degree or equivalent is required.
  • At least 15 years of experience is required; the posting also specifies 15 years of experience in Snowflake Data Warehouse.
  • At least 6 years of experience with Knowledge Graph technologies, including RDF, SPARQL, LPG, and SHACL.
  • At least 6 years of experience in schema design, ontology management, and Knowledge Graph curation.
  • At least 4 years of experience designing and developing Knowledge Graph solutions and graph-based machine-learning models, both functional and technical.
  • At least 3 years of experience implementing end-to-end data pipelines for AI applications, especially large language models, including hands-on design and configuration.
  • At least 6 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, and Amazon Neptune, and vector databases.
  • At least 6 years of managerial experience leading people, explaining the value of semantic layers and knowledge graphs to senior business and technology stakeholders, and a proven track record in sales or pre-sales, delivering data-transformation programs, and developing teams.
  • Practical experience with natural language processing techniques or search techniques, and prompt engineering.
  • Experience with large language models for enterprise-scale applications.
  • At least 5 years of hands-on experience with cloud platforms, including AWS, Azure, or GCP.
  • At least 5 years of experience with Python and frameworks such as TensorFlow and PyTorch, and tools for building ETL pipelines such as Apache NiFi or Airflow.
  • External client-facing consulting experience is required.
  • Fifteen years of full-time education is required.

Responsibilities

  • Design and structure advanced language models capable of understanding and generating natural-language text.
  • Design neural-network architectures and model parameters trained on large quantities of unlabeled text data.
  • Collaborate with teams to refine model parameters and optimize performance, and explore approaches to improve language processing in complex systems.
  • Lead the complete knowledge-engineering scope on strategic, complex programs, including thought leadership, innovation, and discipline assets.
  • Set strategy and architecture for translating real-world problems into scalable AI and knowledge-graph solutions.
  • Lead and develop engineering teams, building capability across the discipline and guiding adoption of methods and algorithms.
  • Define and advance practice standards and translate innovation into new offerings.
  • Set standards for ontology design, evaluation, and maintenance across programs.
  • Shape methodologies and data architectures that demonstrate value at scale.
  • Create thought leadership, innovation, and reusable assets that advance the discipline.
  • Direct end-to-end delivery across complex, multi-stakeholder programs with business and technical leaders.
  • Lead the Knowledge Graph solution scope for strategic, complex clients and transform data architecture.
  • Own the design, development, and implementation of AI and semantic solutions across the program.
  • Partner with project leaders, delivery leads, and senior client stakeholders to create graph-powered offerings.
  • Develop trusted-advisor relationships with senior client stakeholders.
  • Make and defend the business case for semantic-layer solutions at the executive level.
  • Lead sales and pre-sales efforts and shape data-transformation programs.
  • Drive practice thought leadership, innovation, and asset development on trends and agentic technologies, and develop delivery teams.
  • Serve as a subject-matter expert and provide influencing and advisory support.
  • Make team decisions, engage with multiple teams, and contribute to key decisions.
  • Provide solutions to problems that apply across multiple teams.
  • Lead development and implementation of scalable language-model architectures.
  • Mentor and guide junior team members to support their professional growth and technical excellence.
  • Design and optimize data storage and retrieval in cloud-based data-warehouse environments.
  • Integrate large-scale data-processing workflows with language-model training pipelines.
  • Manage and analyze large datasets to support model training and evaluation.

Desired Qualifications

  • Experience with Machine Learning and Data Engineering.
  • Broad experience in diverse machine-learning techniques and agentic systems.

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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