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About Signify Through bold discovery and cutting-edge innovation, we lead an industry that is vital for the future of our planet: lighting. Through our leadership in connected lighting and the Internet of Things, we're breaking new ground in data analytics, AI, and smart solutions for homes, offices, cities, and beyond. At Signify, you can shape tomorrow by building on our incredible 125+ year legacy while working toward even bolder sustainability goals. Our culture of continuous learning, creativity, and commitment to diversity and inclusion empowers you to grow your skills and career. Join us, and together, well transform our industry, making a lasting difference for brighter lives and a better world. More about the role Job Summary Builds and supports scalable Machine Learning (ML) and MLOps systems, enabling reliable model deployment through modern architectures such as microservices and containerized environments. Works closely with data scientists and engineers to operationalize models, improve performance, and integrate intelligent components including multi-agent systems into production workflows. Key Areas of ResponsibilityContribute to the design and implementation of scalable ML infrastructure supporting model training, deployment, and inference. Develop and deploy ML solutions as modular, reusable microservices using APIs, enabling flexible integration with enterprise applications. Package ML models and services using Docker and deploy them using orchestration platforms such as Kubernetes to ensure scalability, portability, and reliability. Monitor production systems for model performance, latency, and drift; support optimization and improvements. Build and maintain CI/CD pipelines for ML workflows, including versioning, testing, and automated deployment. Develop and maintain scalable data pipelines for batch and real-time processing to support model training and inference. Contribute to the development of multi-agent or agent-based solutions (e.g., orchestration of LLM agents, task-specific services, or workflow agents) to solve complex business problems. Troubleshoot issues across distributed ML systems and microservices; ensure system stability and uptime. Follow best practices for secure deployment, API management, and data privacy across distributed systems. Work with engineering, data science, and business teams to deliver scalable, production-ready solutions. Stay updated on emerging technologies in ML, MLOps, distributed systems, and AI (including GenAI and agent-based frameworks). Critical ExperiencesBachelors or Masters degree in Computer Science, Data Science, or a related field. Strong understanding of machine learning algorithms, statistical modeling, and applied data science use cases. Experience with ML lifecycle management and tools such as AWS SageMaker, Azure ML, Dataiku, or similar platforms. Hands-on experience building RESTful APIs and deploying ML models as microservices using frameworks such as FastAPI, Flask, or similar. Working knowledge of Docker for containerization and Kubernetes for service orchestration, scaling, and deployment. Exposure to designing or integrating multi-agent systems, LLM-based agents, or distributed service-based architectures is a strong plus. Experience with data processing frameworks such as Spark, Hadoop, or NoSQL databases. Hands-on experience with AWS or Azure, including deploying and managing scalable ML solutions. Proficiency in Python and SQL; familiarity with Java/Scala is a plus. Everything well do for you You can grow a lasting career here. Well encourage you, support you, and challenge you. Well help you learn and progress in a way thats right for you, with coaching and mentoring along the way. Well listen to you too, because we see and value every one of our 27,000+ people. We believe that a diverse and inclusive workplace fosters creativity, innovation, and a full spectrum of bright ideas. With a global workforce present in 70+ countries, we are dedicated to creating an inclusive environment where every voice is heard and valued, helping us all achieve more together. Come join us, and together we can light up the future. .
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.