Lead ML Operations Engineer

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, we’ll transform our industry, making a lasting difference for brighter lives and a better world. 

More about the role

Job Summary

Develops and maintains Machine Learning (ML) infrastructure to support scalable and efficient model deployment. Monitors and optimizes machine learning models in production environments to ensure high performance and reliability. Collaborates with data scientists and engineers to implement best practices for model integration and data processing.

Key Areas of Responsibility

• Designs and implements scalable machine learning infrastructure for seamless model deployment.
• Monitors and optimizes the performance of machine learning models in production environments.
• Develops automated processes for continuous integration and delivery of machine learning models.
• Ensures data processing pipelines are robust and scalable for model training and prediction.
• Identifies and resolves issues in machine learning workflows to enhance operational efficiency.
• Implements security and compliance measures to protect data integrity and privacy.
• Collaborate with cross-functional teams to develop and implement data-driven solutions that improve business performance. Present findings and recommendations to senior leadership and stakeholders in a clear and concise manner.
• Stays up to date with the latest trends and advancements in ML, MLOps, and artificial intelligence.

Critical Experiences

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Certification in cloud platforms such as AWS Certified Machine Learning or Google Professional Machine Learning Engineer is highly beneficial.
• Deep knowledge on machine learning, data mining and statistical modelling, and extensive experience applying these methods to real world problems. Hands on working experience on at least one of the MLOps Platform like AWS Sagemaker, Dataiku, Data Robot. Experience with big data technologies such as Hadoop, Spark, and NoSQL databases. Hands on experience with cloud computing platforms such as AWS or Azure. Strong programming skills in Python, Java and SQL and various open-source machine learning and analytics packages. - Strong understanding of Machine Learning concepts and algorithms.

Everything we’ll do for you

You can grow a lasting career here. We’ll encourage you, support you, and challenge you. We’ll help you learn and progress in a way that’s right for you, with coaching and mentoring along the way. We’ll 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.