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Key Responsibilities: Perform exploratory data analysis (EDA) to uncover trends, patterns, and actionable insights from large datasets. Translate business problems into analytical and machine learning solutions. Design, develop, train, and optimise machine learning models and algorithms. Evaluate model performance using appropriate validation techniques, hyperparameter tuning, and model optimisation methods. Deploy machine learning models into production environments and integrate them with existing applications through APIs. Collaborate with software engineering and DevOps teams to build scalable, production-ready AI solutions. Monitor deployed models, troubleshoot issues, and continuously improve model accuracy and efficiency. Present analytical findings and recommendations clearly to both technical and non-technical stakeholders. Stay current with advancements in AI, machine learning, and data science, recommending new tools and best practices where applicable. Requirements: Minimum 3 years of hands-on experience in machine learning implementation, deployment, and statistical data analysis involving large datasets. Strong experience translating business requirements into analytical solutions using statistical and machine learning techniques. Proficient in:PythonSQLData WranglingData VisualisationDatabase Management Systems (DBMS) Experience with one or more of the following:TensorFlowPyTorchRSASQlik Sense Experience deploying machine learning models into production environments. Familiarity with Agile development methodologies such as Scrum is preferred. Nice to Have: Experience with any of the following technologies would be advantageous: Power BI Microsoft Access SharePoint Kubernetes Docker or Podman Microservices Architecture DevSecOps practices Work Location: East / West (EA Reg No: 20C0312) Please email a copy of your detailed resume to chally@talentsis.com.sg for immediate processing. Only shortlisted candidates will be notified.
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.