Knowledge Engineer / Semantic Expert for AI Associate Director
for section headings. Use - for any list of duties/requirements/benefits, and
for prose.
We need to preserve all substantive info, not invent or summarize away detail. Remove duplicated lines.
We need to include sections only when posting has real specific content.
Thus sections: About the role (intro paragraph), Responsibilities (the work description), Requirements (education/experience), Qualifications maybe overlapping; but we can combine Requirements and Qualifications? The instruction says use headings for those categories if content exists. We have both requirements and qualifications; we can separate: Requirements (education/experience minima) and Qualifications (bonus points, PhD, etc). Skills maybe separate list of technical skills.
But we must avoid thin sections; better to combine into a few substantive sections. However we need to follow instruction: "Prefer a few substantive sections over many thin ones." So we could have About the role, Responsibilities, Requirements & Qualifications (combined), Benefits, Pay, Schedule (travel). That's 6 sections.
Let's extract content precisely.
About the role: first paragraph(s) up to "You Are As a Knowledge Engineer..." Actually the "You Are" starts responsibilities. So About the role could be the initial description about Accenture and team. Let's take: "We Are Accenture is helping companies use generative AI and semantic layer to reinvent their enterprise and optimize business functions for breakthrough innovation and competitive advantage. With over 1,600 professionals dedicated to generative AI, leveraging the depth and experience of more than 40,000 AI and data professionals across the company our Advanced Data & AI team brings together our Experienced Innovation, Strategic Investment, Exceptional Talent, and Power Ecosystem."
That's one paragraph.
Responsibilities: combine the paragraphs starting with "You Are As a Knowledge Engineer..." through the travel sentence. Let's gather:
"You Are As a Knowledge Engineer, you formulate real-world problems into practical, efficient, and scalable AI and Knowledge Graph problems. You lead a team and provide guidance to explore and implement new methodologies, model building techniques, and cutting-edge algorithms, and applying these techniques with the right architecture to solve real-world problems.You have a deep understanding and ability to remain at the forefront of knowledge engineering, generative AI, LLM, and multi-modal models (with a focus on driving innovation by applying these techniques to new business problems, use cases, and scenarios).As needed by the specific problem, you design, evaluate, and maintain ontologies.As a significant part of this role, you will be justifying the value of innovative generative AI and knowledge graph approaches in the business problems, and you'll be expected to construct methodologies and data architectures that clearly demonstrate their value.You'll also work collaboratively with teams from both the business and technical side, including users, use case representatives, business owners, engineers, architects, and UI designers, to achieve end-to-end project development goals.The Work Build Knowledge Graph solutions that transform clients’ data architecture.Design, develop, and implement AI and semantic solutions and ensure that all the pieces work together seamlesslyWork with the project team, team leaders, project delivery leads, and client stakeholders to create stand-out Data & AI offerings powered by graph-based technologiesDevelop strong relationships with clients and gain the trust of key advisorsMake the business case for the semantic layer solution recommended to the clientPitch in on Accenture sales efforts when neededContinue to learn and develop cutting edge Data & AI solutions, especially agentic technologies, provide through leadership on technology trends, new opportunities and innovations, or foreseeable limitations, risks, and concerns. Travel may be required for this role. The amount of travel will vary from 0% to 100% depending on business need and client requirements."
We need to split into sentences with proper spacing. We'll keep as is but maybe add spaces where missing.
Now Requirements & Qualifications: combine education/experience requirements and bonus points.
Let's extract:
"Bachelor's degree or equivalent (minimum 12 years' work experience). If Associate’s Degree, must have equivalent minimum 6-year work experienceMinimum of 4 years of experience in designing and developing knowledge graph solutions and graph-based machine learning models, functional and technical experience required.Minimum of 3 end-to-end data pipeline implementation for AI applications, particularly those involving LLMs or similar models, including hands-on design and configurationMinimum of 6 years strong knowledge of relational databases, object stores, graph databases (e.g. Stardog, Neo4J, Amazon Neptune), and vector databasesMinimum of 6 years of experience in Knowledge Graph technologies (e.g. RDF, SPARQL, LPG, SHACL)Minimum of 6 years of experience with schema design, ontology management, and Knowledge Graph curation.Minimum of 6 years of managerial experience working with people with the ability to clearly explain the value of semantic layer and knowledge graphs to senior business and technology stakeholders, and proven track record selling and /or pre-sales and delivering data transformation programs and developing teams. Bonus Points If Experience in Practical experience with NLP techniques and/or Search Techniques, prompt engineeringExperience with LLMs for enterprise-scale applications.5+ years of hands-on experience with cloud platforms (AWS, Azure, GCP)5+ years of experience in Python, with experience in frameworks like Tensorflow, PyTorch, and tools for building ETL pipelines (e.g. Apache NiFi, Airflow)Strong collaboration skills with the ability to work across engineering, research, and product teams across multiple time zones.You have external client-facing consulting experiencePh.D. in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.Broad experience in diverse ML techniques and agentic systems."
We need to split into list items for each requirement. Could be long list.
We'll create
with each bullet as a requirement.
We need to preserve all substantive info; not invent.
We'll separate each distinct requirement.
Let's list:
- Bachelor's degree or equivalent (minimum 12 years' work experience)
- If Associate’s Degree, must have equivalent minimum 6-year work experience
- Minimum of 4 years of experience in designing and developing knowledge graph solutions and graph-based machine learning models, functional and technical experience required
- Minimum of 3 end-to-end data pipeline implementation for AI applications, particularly those involving LLMs or similar models, including hands-on design and configuration
- Minimum of 6 years strong knowledge of relational databases, object stores, graph databases (e.g. Stardog, Neo4J, Amazon Neptune), and vector databases
- Minimum of 6 years of experience in Knowledge Graph technologies (e.g. RDF
for prose. We need to preserve all substantive info, not invent or summarize away detail. Remove duplicated lines. We need to include sections only when posting has real specific content. Thus sections: About the role (intro paragraph), Responsibilities (the work description), Requirements (education/experience), Qualifications maybe overlapping; but we can combine Requirements and Qualifications? The instruction says use headings for those categories if content exists. We have both requirements and qualifications; we can separate: Requirements (education/experience minima) and Qualifications (bonus points, PhD, etc). Skills maybe separate list of technical skills. But we must avoid thin sections; better to combine into a few substantive sections. However we need to follow instruction: "Prefer a few substantive sections over many thin ones." So we could have About the role, Responsibilities, Requirements & Qualifications (combined), Benefits, Pay, Schedule (travel). That's 6 sections. Let's extract content precisely. About the role: first paragraph(s) up to "You Are As a Knowledge Engineer..." Actually the "You Are" starts responsibilities. So About the role could be the initial description about Accenture and team. Let's take: "We Are Accenture is helping companies use generative AI and semantic layer to reinvent their enterprise and optimize business functions for breakthrough innovation and competitive advantage. With over 1,600 professionals dedicated to generative AI, leveraging the depth and experience of more than 40,000 AI and data professionals across the company our Advanced Data & AI team brings together our Experienced Innovation, Strategic Investment, Exceptional Talent, and Power Ecosystem." That's one paragraph. Responsibilities: combine the paragraphs starting with "You Are As a Knowledge Engineer..." through the travel sentence. Let's gather: "You Are As a Knowledge Engineer, you formulate real-world problems into practical, efficient, and scalable AI and Knowledge Graph problems. You lead a team and provide guidance to explore and implement new methodologies, model building techniques, and cutting-edge algorithms, and applying these techniques with the right architecture to solve real-world problems.You have a deep understanding and ability to remain at the forefront of knowledge engineering, generative AI, LLM, and multi-modal models (with a focus on driving innovation by applying these techniques to new business problems, use cases, and scenarios).As needed by the specific problem, you design, evaluate, and maintain ontologies.As a significant part of this role, you will be justifying the value of innovative generative AI and knowledge graph approaches in the business problems, and you'll be expected to construct methodologies and data architectures that clearly demonstrate their value.You'll also work collaboratively with teams from both the business and technical side, including users, use case representatives, business owners, engineers, architects, and UI designers, to achieve end-to-end project development goals.The Work Build Knowledge Graph solutions that transform clients’ data architecture.Design, develop, and implement AI and semantic solutions and ensure that all the pieces work together seamlesslyWork with the project team, team leaders, project delivery leads, and client stakeholders to create stand-out Data & AI offerings powered by graph-based technologiesDevelop strong relationships with clients and gain the trust of key advisorsMake the business case for the semantic layer solution recommended to the clientPitch in on Accenture sales efforts when neededContinue to learn and develop cutting edge Data & AI solutions, especially agentic technologies, provide through leadership on technology trends, new opportunities and innovations, or foreseeable limitations, risks, and concerns. Travel may be required for this role. The amount of travel will vary from 0% to 100% depending on business need and client requirements." We need to split into sentences with proper spacing. We'll keep as is but maybe add spaces where missing. Now Requirements & Qualifications: combine education/experience requirements and bonus points. Let's extract: "Bachelor's degree or equivalent (minimum 12 years' work experience). If Associate’s Degree, must have equivalent minimum 6-year work experienceMinimum of 4 years of experience in designing and developing knowledge graph solutions and graph-based machine learning models, functional and technical experience required.Minimum of 3 end-to-end data pipeline implementation for AI applications, particularly those involving LLMs or similar models, including hands-on design and configurationMinimum of 6 years strong knowledge of relational databases, object stores, graph databases (e.g. Stardog, Neo4J, Amazon Neptune), and vector databasesMinimum of 6 years of experience in Knowledge Graph technologies (e.g. RDF, SPARQL, LPG, SHACL)Minimum of 6 years of experience with schema design, ontology management, and Knowledge Graph curation.Minimum of 6 years of managerial experience working with people with the ability to clearly explain the value of semantic layer and knowledge graphs to senior business and technology stakeholders, and proven track record selling and /or pre-sales and delivering data transformation programs and developing teams. Bonus Points If Experience in Practical experience with NLP techniques and/or Search Techniques, prompt engineeringExperience with LLMs for enterprise-scale applications.5+ years of hands-on experience with cloud platforms (AWS, Azure, GCP)5+ years of experience in Python, with experience in frameworks like Tensorflow, PyTorch, and tools for building ETL pipelines (e.g. Apache NiFi, Airflow)Strong collaboration skills with the ability to work across engineering, research, and product teams across multiple time zones.You have external client-facing consulting experiencePh.D. in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.Broad experience in diverse ML techniques and agentic systems." We need to split into list items for each requirement. Could be long list. We'll create
- with each bullet as a requirement.
We need to preserve all substantive info; not invent.
We'll separate each distinct requirement.
Let's list:
- Bachelor's degree or equivalent (minimum 12 years' work experience)
- If Associate’s Degree, must have equivalent minimum 6-year work experience
- Minimum of 4 years of experience in designing and developing knowledge graph solutions and graph-based machine learning models, functional and technical experience required
- Minimum of 3 end-to-end data pipeline implementation for AI applications, particularly those involving LLMs or similar models, including hands-on design and configuration
- Minimum of 6 years strong knowledge of relational databases, object stores, graph databases (e.g. Stardog, Neo4J, Amazon Neptune), and vector databases
- Minimum of 6 years of experience in Knowledge Graph technologies (e.g. RDF