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YOU ARE You lead a significant knowledge engineering scope and function within a large program; actively contribute to thought leadership. You shape how a substantial body of work formulates real-world problems into scalable AI and KG solutions, and set the technical direction for that scope. You lead a team and guide exploration and implementation of new methodologies, model building techniques, and cutting-edge algorithms. You remain at the forefront and drive innovation by applying these techniques to new business problems, use cases, and scenarios. You design, evaluate, and maintain ontologies, and establish the standards others follow. You justify the value to business and technology stakeholders, and construct the methodologies and data architectures that demonstrate it. You publish or present perspective beyond the immediate project. You work across business and technical teams to drive end-to-end delivery for their scope. THE WORK Leads the build of Knowledge Graph solutions that transform a client's data architecture within program scope. Directs the design, development, and implementation of AI and semantic solutions, ensuring all the pieces work together seamlessly. Works with project leaders, delivery leads, and client stakeholders to create stand-out, graph-powered Data & AI offerings. Develops strong client relationships and earns the trust of key advisors. Makes the business case for the semantic layer solution recommended to the client. Contributes meaningfully to Accenture sales and pre-sales efforts. Provides thought leadership on technology trends, new opportunities, innovations, and foreseeable limitations, risks, and concerns. Basic (required) Qualification Bachelor's degree or equivalent, plus at least 4 of the following: Experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL) demonstratable experience in schema design, ontology management, and KG curation Solid experience in designing and developing KG solutions and graph-based ML models (functional + technical) Familiarity with end-to-end data pipeline implementations for AI applications (esp. LLMs), with hands-on design and configuration experience Considerable experience and strong knowledge of relational databases, object stores, graph databases (Stardog, Neo4J, Amazon Neptune), and vector databases Experience in leading a team or workstream within a larger program Preferred Qualification Experience with cloud platforms (AWS, Azure, GCP) Knowledge of Python, with experience in frameworks like Tensorflow, PyTorch, and tools for building ETL pipelines (e.g. Apache NiFi, Airflow) Practical experience with NLP and/or Search techniques Prompt engineering, and LLMs for enterprise-scale applications. Strong collaboration skills with the ability to work across engineering, research, and product teams across multiple time zones. You have external client-facing consulting experience Ph.D. in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field. Broad experience in diverse ML techniques and agentic systems.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.