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DigiKey is one of the fastest growing distributors of electronic components in the world. Since its founding in 1972, DigiKey has been committed to offering the broadest selection of in-stock electronic components, as well as providing the best service possible to its customers, aiding engineers through the entire design process, from Prototype to Production. This has led the company to be highly ranked year after year in industry surveys in North America as well as Europe and Asia, in categories covering such facets of business as availability of products, speed of service, responsiveness to problems, and more. Key Responsibilities Build and maintain end-to-end ML pipelines (train validate deploy monitor)Productionize models for:Batch scoring (scheduled pipelines)Real-time APIs (Kubernetes-hosted services)Standardize deployments using:Dataiku (Automation + API nodes)Containerized services on Azure Kubernetes Service (AKS)Implement CI/CD pipelines (Azure DevOps/GitHub Actions) for ML workflowsEstablish monitoring and alerting:Model performance, drift, failures, latencyOperationalize GenAI systems (LangChain/RAG):Prompt/version control, evaluation pipelines, tracing, cost controlsDefine and enforce model governance:Model registry, approvals, auditability, documentationBuild reusable templates and paved roads for data scientistsRequired Qualifications Bachelors degree in Computer Science, Engineering, or related field (16 years of formal education).5 to 7 years of overall IT experience with 3+ years in Model OpsStrong Python + software engineering practices (testing, Git, modular code)Experience deploying ML systems in batch and real-time environmentsHands-on with Docker + Kubernetes (AKS preferred)Experience with CI/CD pipelines (Azure DevOps or GitHub Actions)Experience implementing monitoring/alerting for production systemsCollaboration & Support Act as the primary platform contact for Model Ops.Provide support and participate in incident response and root-cause analysis.Mentor junior engineers to enable L1/L2 support and contribute to internal platform standards.Preferred Qualifications Dataiku DSS (Automation node, API node, scenarios)Azure services (ML, Storage, Key Vault, Monitor)MLflow or model registry experienceLangChain / RAG / vector databasesObservability tools (Monte Carlo, Langsmith, Datadog or equivalents)All personal data collected will be used for recruitment purposes only. All unsuccessful applications will be deleted within 24 months. .
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.