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About the Role :We are looking for an AI Engineer to design and develop machine learning, statistical analytics, and agentic AI solutions for semiconductor manufacturing and engineering applications. The role combines software engineering, applied machine learning, deep learning, computer vision, and modern AI frameworks to build intelligent systems that improve product performance, defect detection, engineering productivity, and manufacturing insights.The engineer is expected to contribute independently, lead small to medium-sized technical initiatives, and collaborate across software, algorithms, systems, data science, and product teams. Given the product is a large data analytics solution for the semi industry, proficiency in C++ is necessary in addition to python.Key Responsibilities :Machine Learning & Statistical Analytics :- Design and implement machine learning solutions for classification, regression, clustering, anomaly detection, and forecasting.- Develop statistical models using:1. Hypothesis testing2. Regression analysis3. Bayesian methods4. PCA5. Time-series analysis6. Design of Experiments (DOE)7. Predictive analytics- Analyze large-scale manufacturing and engineering datasets to derive actionable insights.- Evaluate model performance and drive continuous improvements.Deep Learning & Computer Vision :- Develop deep learning models for:1. Defect classification2. Pattern recognition3. Image segmentation4. Object detection5. Feature extraction- Build and optimize models using:1. PyTorch2. TensorFlow3. ONNX- Work with image-processing pipelines and semiconductor inspection datasets.Agentic AI & LLM Applications :- Develop AI assistants and workflow automation solutions using modern agent frameworks.- Build Retrieval-Augmented Generation (RAG) pipelines.- Integrate LLMs with enterprise data sources and engineering workflows.- Develop tool-using AI agents capable of planning, reasoning, and workflow execution.- Contribute to multi-agent systems for engineering productivity and analytics.Software Engineering :- Design and implement production-quality software in:1. C++2. Python- Convert business and system requirements into scalable AI solutions.- Develop reusable software libraries, APIs, and services.- Participate in code reviews and software design reviews.- Optimize performance, scalability, and reliability of AI services.Collaboration & Product Development:- Work closely with:1. Software Engineers2. Algorithm Engineers3. Product Managers4. Applications Teams- Contribute to solution design discussions.- Participate in feasibility studies and technical investigations.- Support deployment, validation, and customer adoption activities.Required Qualifications:Experience:- Typically:1. BS + 5 years experience2. OR MS + 3 years experience3. OR PhD with relevant experience- Consistent with the experience expectations for the P3 AI Engineering level.Required Technical Skills:Programming:- Strong proficiency in:1. Python2. Up-to-date C++ (C++14/17/20)- Experience with:1. Data structures2. Algorithms3. Object-oriented design4. Multithreaded programmingMachine Learning:- Experience developing and deploying:1. Random Forests2. XGBoost3. Gradient Boosting4. Isolation Forest5. Clustering Algorithms6. Predictive ModelsDeep Learning:- Hands-on experience with:1. PyTorch2. TensorFlow3. Deep Neural Networks4. CNNs5. Transformers6. Vision ModelsStatistical Analytics:- Strong understanding of:1. Probability & Statistics2. Experimental Design3. Statistical Inference4. Feature Engineering5. Data Validation6. Model EvaluationAgentic AI:- Exposure to one or more:1. LangGraph2. LangChain3. AutoGen4. CrewAI5. Semantic Kernel6. OpenAI Agents SDK7. MCP (Model Context Protocol)- Skills include:1. Tool Calling2. Workflow Orchestration3. RAG Systems4. Knowledge Retrieval5. AI AutomationWhat Success Looks Like:- Independently deliver AI and machine-learning features.- Design and deploy production-ready statistical and deep learning models.- Build agentic AI capabilities that improve engineering productivity.- Contribute to image-processing and advanced analytics solutions.- Influence architecture and technical design decisions.- Mentor junior engineers and contribute to engineering best practices. (ref:hirist.tech) .
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.