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YOU ARE You are a a strong individual contributor knowledge engineer growing into a team lead. You are well versed in the full knowledge graph development lifecycle. You formulate real-world problems into practical, efficient, scalable AI and Knowledge Graph solutions, working hands-on across the lifecycle from ingestion through modeling, curation, and deployment. You apply current methodologies, techniques, and algorithms with the right architecture, and begin to guide junior engineers on the project. You stay current with knowledge engineering, generative AI, LLM, and multi-modal models; look for opportunities to apply them to the problem at hand. You design, evaluate, and maintain ontologies as needed. You help articulate the value of generative AI and knowledge graph approaches for a given business problem. You share what they learn with the team. You collaborate with users, use case reps, engineers, architects, and UI designers to deliver their piece of an end-to-end solution. THE WORK Build Knowledge Graph components that contribute to transforming a client's data architecture. Design, develop, and implement AI and semantic solutions; ensure their work integrates cleanly with the broader system. Work alongside the project team and delivery leads. Build solid working relationships with client counterparts on their workstream. Help assemble the supporting evidence for the recommended semantic layer solution. Support Accenture sales efforts when called on. Keep developing skills in cutting-edge Data & AI solutions, especially agentic technologies, and shares with the team. Basic (required) Qualification Bachelor's degree or equivalent, plus at least 3 of the following: Experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL) Experience in schema design, ontology management, and KG curation Well versed in designing and developing KG solutions and graph-based ML models (functional + technical) Proven experience with end-to-end data pipeline implementation for AI applications (esp. LLMs), with hands-on design and configuration Strong knowledge and working experience with relational databases, object stores, graph databases (Stardog, Neo4J, Amazon Neptune), and vector databases (no leadership/commercial requirement at this band) Preferred Qualification Hands-on experience with cloud platforms (AWS, Azure, GCP) Well versed in 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. You have team lead experience 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 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.