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Responsibilities Data Requirement Gathering: Partner with supply chain functional leads, Internal Data Platform teams, and AI/ML engineers to elicit and document data requirements and translate them into scalable pipeline and schema designs, ensuring every dataset delivers measurable business value. Pipeline & Platform Engineering: Act as the primary technical lead for data foundation engineering. Design, build, and maintain ingestion, transformation, and storage pipelines that reliably deliver clean, structured, and timely data to downstream AI/ML consumers within the supply chain GCP space. Graph-Based Data Modeling: Work closely with Knowledge Graph engineering and AI teams to design, construct, and maintain ontologies and graph schemas against enterprise data sources, enabling decisionintelligence frameworks that proactively identify and mitigate risks across the global Ntier supplier network. Build and maintain the data pipelines that keep these graphs continuously updated with data from ERP, logistics, and supplier systemsthe foundation for 'what-if' scenario simulation using Generative AI and Graph analytics. AI-Driven SDLC Execution: Champion and implement AIassisted development practices. Implement agentic workflows (e.g., AutoGen, CrewAI) and use LLMbased tools (e.g., GitHub Copilot, automated PR agents, and AIgenerated documentation) to accelerate delivery with high code quality for the Decision Intelligence platform. Pipeline & DataOps Engineering: Design the 'connective tissue' between source systems, Knowledge Graph updates, and model inference engines. Establish rigorous data validation, versioning, and observability frameworks. Maintain automated pipelines that ensure decisionsupport tools are always powered by the most current, highquality data. Technical Standardization: Develop reusable data contracts, schemas, and ingestion patterns to ensure that data infrastructure can be scaled across multiple business units without redundant engineering effort. Requirements Bachelors degree in Computer Science, Data Science, or a related technical field. 3+ years of progressive experience in AI/ML, Data Engineering, or Data Science, with a proven track record of delivering productiongrade solutions in large enterprise environments. Strong proficiency in Python and SQL. Deep experience with distributed data processing frameworks (e.g., Spark, Beam, Dataflow) and Graph Query Languages (e.g., Cypher, Gremlin). Demonstrated experience with data pipeline orchestration tools (e.g., Airflow, Dagster, Cloud Composer) and CI/CD for data pipelines, and designing/implementing AIspecific SDLCs. Strong understanding of data modeling (relational, dimensional, and graph), data warehousing concepts, and building data foundations that support LLM/RAG applications including chunking strategies, embedding pipelines, and vector store integration. Strong technical expertise in cloud services (GCP/BigQuery/Dataflow/Cloud Storage) and data integration patterns. Strong analytical, problemsolving, and criticalthinking skills. Exceptional communication & interpersonal skills, to translate complex AI logic into strategic recommendations for supply chain business leaders. Core Competencies Demonstrates expertise in Data Engineering and AI/ML practices, with a strong focus on designing and maintaining data pipelines, data modeling, and implementing AIdriven development workflows. Proficient in cloud services and data integration patterns to support scalable and highquality data solutions. .
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.