- Lead the end-to-end development of machine learning models, from data collection and preprocessing to model training, evaluation, and deployment.
- Apply machine learning techniques and algorithms, such as deep learning, Generative AI, natural language processing, reinforcement learning, etc on finance datasets.
- Design and implement scalable machine learning algorithms and systems that can efficiently handle large volumes of data.
- Collaborate with data scientists, data engineers and business stakeholders to understand business requirements, translate them into technical requirements and deliver end-to-end data solutions.
- Develop robust, production-grade code for deploying machine learning models on cloud platforms. Ensure solutions meet reliability, performance, and security standards.
- Implement best practices for model monitoring, performance optimization, and continuous integration/deployment.
- Mentor and coach junior machine learning engineers, data engineers and data scientists, sharing best practices and knowledge.
- Research and stay updated with the latest developments and trends in machine learning and related fields and establish industry network by participating in internal and external forums, conferences, etc.
Qualifications:
Requirements (Key) - Bachelor's degree or higher in Computer Science, Engineering, or an equivalent qualification.
- 3-5 years of proven experience deploying machine learning models in a production environment.
- Proficiency in Python, Bash and SQL with intermediate understanding of C/C++/Java/Scala/R.
- Experience in distributed computing frameworks such as Ray, Dask, and Spark.
- Experience with deploying Machine Learning models to AWS cloud platform.
- Experience in adhering to software engineering best practices, including writing clean code.
- Experience with CI/CD tools and software, including Jenkins, Git, Bitbucket, Spinnaker and Helm.
- Strong team player and you can work effectively in a collaborative, fast paced, high achieving environment.
- Exceptional analytical and problem-solving skills.
- Good communication and presentation skills.
Requirements (Preferred) - Exposure to Deep learning frameworks such as PyTorch and TensorFlow.
- Familiarity with machine learning pipeline management and model monitoring tools (MLflow, Kubeflow, etc).
Certifications (Preferred) - AWS certification (e.g. Solutions Architect, DevOps Engineer)
- Kubernetes certification (e.g. Certified Kubernetes Application Developer (CKAD), Certified Kubernetes Administrator (CKA))
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Machine Learning Engineer - Singapore, Singapore - Johnson & Johnson
Description
Description:
At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at
An opportunity has arisen for an extraordinary and enthusiastic Machine Learning Engineer. This role requires a deep understanding of machine learning algorithms, strong software engineering skills, and the ability to translate business requirements into scalable machine learning solutions.
As a member of the Global Finance Data Science team, you will report to the Principal Machine Learning Engineer. You will be involved in designing, developing and deploying scalable and efficient Machine Learning models and systems on cloud platforms. You will be working closely with Data Scientists to understand technical requirements in Machine Learning projects and devising solutions to meet those needs.
Responsibilities
