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Dover is a diversified global manufacturer with annual revenue of over $8 billion. We deliver innovative equipment and components, specialty systems, consumable supplies, software and digital solutions, and support services through five operating segments: Engineered Products, Clean Energy & Fueling, Imaging & Identification, Pumps & Process Solutions and Climate & Sustainable Technologies. Dover combines global scale with operational agility to lead the markets we serve. Recognized for our entrepreneurial approach for over 60 years, our team of approximately 24,000 employees takes an ownership mindset, collaborating with customers to redefine what's possible. Headquartered in Downers Grove, Illinois, Dover trades on the New York Stock Exchange under "DOV." Additional information is available atdovercorporation.com. Position: AI Solution Engineer Experience: 3-7 Years Education: B.E Location: Bangalore Roles & Responsibilities: Collaborate with business stakeholders to understand requirements, identify opportunities, and translate business needs into technical solutions. Solution Design & Architecture Design, develop, and deploy end-to-end AI solutions that deliver measurable business value. Contribute to solution architecture decisions, technical design reviews, technology selection, and implementation roadmaps. Implement AI, software engineering, security, governance, and DevOps best practices throughout the development lifecycle. AI Application Development & Enterprise Integration Build AI-powered applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, Copilot technologies, and emerging Agentic AI frameworks. Develop scalable web applications, APIs, microservices, and backend services to support AI and digital transformation initiatives. Integrate AI solutions with enterprise platforms such as SAP S/4HANA, Salesforce, SharePoint, Microsoft 365, Azure services, PLM systems, MES platforms, CRM systems, and other business applications. Design and implement secure data pipelines, orchestration workflows, and enterprise connectors required to operationalize AI solutions. Act as a versatile AI engineer, contributing to data science, machine learning engineering, and data engineering activities needed to turn AI ideas into production-ready business solutions. Develop intelligent automation solutions that optimize business processes and improve employee productivity. Delivery, Deployment & Production Support Develop and maintain production-grade applications using agile software development methodologies. Deploy and operationalize machine learning models, Python-based analytics scripts, and AI solutions, such as sales forecasting models, in Azure production environments using appropriate testing, versioning, CI/CD, monitoring, security, and support practices. Support deployment, monitoring, troubleshooting, and continuous improvement of AI solutions in production environments. Drive proof-of-concepts through full production deployment while maintaining high standards of quality, security, and maintainability. Must have skills: Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Information Systems, or a related technical field. 3-7 years of experience developing enterprise software applications, AI solutions, or digital products. Strong software engineering foundations including object-oriented programming, software architecture, design patterns, testing, and maintainable code practices. Proficiency in Python and experience developing production-grade applications. Hands-on experience deploying machine learning models, Python scripts, or analytics solutions into production environments, preferably using Azure services, CI/CD pipelines, model monitoring, and MLOps practices. Hands-on experience deploying and supporting production AI solutions, including solution evaluation, testing, monitoring, governance, continuous improvement, and operational support. Experience developing APIs, REST services, microservices, and enterprise integrations. Practical experience with Generative AI technologies, including Large Language Models (LLMs), Prompt Engineering, Embeddings, Retrieval-Augmented Generation (RAG), Semantic Search, and AI Agents. Experience with at least one enterprise AI development platform such as Azure OpenAI, Azure AI Foundry, OpenAI, Anthropic, Semantic Kernel, LangChain, LlamaIndex, or equivalent. Experience integrating enterprise applications through APIs, middleware, event-driven architectures, or integration frameworks. Good understanding of cloud services and AI platform capabilities, preferably within the Microsoft Azure ecosystem. Experience with Git, CI/CD, DevOps practices, automated testing, and release management. Experience designing and working with relational and non-relational databases. Experience with data engineering fundamentals, including ETL/ELT .
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.