🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
We are looking for a Senior AI/ML Engineer / Data Scientist with strong hands-on expertise in Classical Machine Learning, Statistical Modelling, Statistics, and Optimization, along with working knowledge of LLMs and Generative AI. The role will primarily focus on ML modelling, statistical analysis and optimization solutions for complex business problems. GenAI/LLM will be a secondary component. Skill focus: 70% Classical ML / Statistics / Optimization | 30% LLM / GenAI Key Responsibilities - Translate complex business problems into ML, statistical and optimization solutions. - Design, develop and implement classical machine learning models. - Perform statistical analysis, predictive modelling and optimization. - Apply appropriate ML algorithms based on business objectives and data characteristics. - Perform data exploration, feature engineering, model development and validation. - Develop optimization models for business decision-making and operational problems. - Evaluate and improve model accuracy, robustness and scalability. - Build prototypes and convert successful analytical solutions into production-ready solutions. - Apply LLM and Generative AI techniques to selected business use cases. - Work with LLM APIs, prompt engineering and basic RAG/GenAI architectures. - Collaborate with Data Engineers, Backend Engineers and Frontend Engineers on end-to-end solutions. - Support model deployment, testing, documentation and client handover. - Present technical approaches, analytical findings and recommendations to business stakeholders. - Adapt ML solutions based on changing business requirements. Required Skills - 6+ years of experience in Data Science, Machine Learning, AI/ML Engineering or related areas. - Strong hands-on experience in Classical Machine Learning. - Strong understanding of statistics, mathematics and statistical modelling. - Hands-on experience with optimization techniques and modelling. - Advanced Python programming skills. - Strong experience with Pandas, NumPy and Scikit-learn. - Experience with supervised and unsupervised learning. - Strong understanding of feature engineering, model selection, evaluation and validation. - Proven experience solving real-world business problems using ML/statistical techniques. - Working knowledge of LLMs and Generative AI. - Experience with LLM APIs and prompt engineering. - Strong analytical, problem-solving and communication skills. Positive to Have - Azure or other cloud platform experience. - Azure ML or similar ML platforms. - Time-series modelling and forecasting. - Operations Research / Decision Science experience. - RAG and vector database exposure. - Experience integrating ML models into applications. - Consulting or client-facing experience. - Experience developing optimization/business decision-support solutions. Not Mandatory - MCP Server - Deep Agentic AI - Multi-Agent orchestration - Extensive LLM architecture experience Ideal Candidate The ideal candidate is a Senior Data Scientist / ML Engineer who is strong in traditional Machine Learning, Statistics, Statistical Modelling and Optimization, with sufficient GenAI knowledge to contribute to the LLM component. Candidates who are primarily LLM/GenAI developers without strong Classical ML and Optimization experience should not be prioritized. Interview Focus - Machine Learning fundamentals - Statistics and statistical modelling - Optimization techniques - Python and ML implementation - Real-world ML problem solving - Model selection and evaluation - Business problem ML solution approach - LLM/GenAI fundamentals - Productionization of ML solutions - Client/stakeholder communication .
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.