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Role OverviewWe are seeking a Machine Learning Engineer to lead the end-to-end development of production-grade analytical applications. This is a high-impact role requiring a blend of deep statistical modeling and machine learning. You will be responsible transforming raw consolidated data into high-accuracy forecasts through advanced feature engineering, rigorous model selection, and statistical validation. ResponsibilitiesFeature Engineering & Discovery : Design and build complex feature sets for diverse problem types, including behavioural features for churn, sensor-based lags for maintenance, and seasonal encodings for demand forecasting.Model Selection & Optimization : Conduct systematic experimentation across diverse algorithms (e.g., XGBoost, LightGBM, Prophet, or Deep Learning) to identify the best-performing models.Model Training & Testing : Develop, train, tune, and test a variety of ML architectures including time-series, classification and regression.Statistical Validation & Evaluation : Define and track complex evaluation metrics tailored to manufacturing, such as MAPE, RMSE, etc., while performing deep-dive bias-variance analysis.EDA & Research : Perform exploratory data analysis on consolidated "Gold" layer data to uncover hidden drivers of business outcomes and identify correlations between external signals.Refinement & Performance Tuning : Address critical modeling challenges including bias-variance tradeoffs, class imbalance, and overfitting to ensure models generalize to real-world production data.Skills & Requirements3+ Years of Experience : Proven track record of developing and delivering production-grade ML models across multiple domains (Sales, Finance, Manufacturing, or Supply Chain).Mastery of the Python Ecosystem : Expert-level skills in Pandas, NumPy, Scikit-learn, and SciPy.Advanced Algorithmic Knowledge : Deep expertise in supervised and unsupervised learning, specifically ensemble methods (Boosting/Bagging) and time-series frameworks.Statistical Foundations : Strong grasp of hypothesis testing, probability distributions, and the mathematical principles behind model evaluation and optimization.SQL Proficiency : Expert ability to manipulate data within consolidated database layers to create the "Silver" feature sets required for training.Education : Bachelors or Masters degree in a quantitative field (e.g., Data Science, Statistics, Mathematics, or Computer Science).Cloud Awareness : Experience with Azure Machine Learning or similar cloud modelling environments.Engineering Familiarity : Basic understanding of Docker, MLflow, or FastAPI for handing models off to deployment teams.Personal AttributesStrong problem-solving skills with a passion for data architecture.Excellent communication skills with the ability to explain complex data concepts to non-technical stakeholders.Highly collaborative, capable of working with cross-functional teams.Ability to thrive in a fast-paced, agile environment while managing multiple priorities effectively.CompetenciesTech Savvy - Anticipating and adopting innovations in business-building digital and technology applications.Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.Customer Focus - Building strong customer relationships and delivering customer-centric solutions.Optimize Work Processes - Knowing the most effective and efficient processes to get things done, with a focus on continuous improvement.Why Join Us Be part of a collaborative and agile team driving cutting-edge AI and data engineering solutions.Work on impactful projects that make a difference across industries.Opportunities for professional growth and continuous learning.Competitive salary and benefits package. .
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.