Senior ML Engineer (Freelance)

In this role, you have the opportunity to
apply the newest AI/ML techniques to shape and scale to state-of-the-art inventions in the field of health and well-being. As AI and machine learning are relevant to every business in the industry, our team is shaping the future of next-generation AI tools and platforms in the medical domain. As we have a wide portfolio of ML innovations, you should help in scaling up our ML systems and algorithms working with petabytes of data, various computational options, and setup state-of-the-art MLOps systems. You will have the opportunity to guide the development of Machine Learning Platform to help it become the platform of choice for more than 600 data scientists and AI engineers.

You are responsible for

• Build and deliver architecturally and operationally sound, scalable, and resilient ML solutions in the exciting fields of deep learning, natural language processing, image processing etc.
• Shape and prototype innovative approaches on MLOps, driving best-practices including quality, security, responsible and ethical AI/ML, and operational excellence.
• Interact with various stakeholders including data, ML scientists, Sw engineers, architects, etc.
• Help drive our technical culture and thought leadership on MLOps practices
• Ability to distill vague product input into concrete problem definitions.
• Contributes to (AI/MLOps) technology roadmaps and other strategic related activities.
• Connect to the AI/ML community and academic field to exchange knowledge.
  Inspires his/her team members to get the job done.
   

You are a part of
The Philips Research Software Concepts department, developing advanced solutions for use in hospitals worldwide. The department consists of 90+ professionals. These professionals have state of the art knowledge and are specialized in the field of next generation cloud computing, distributed, decentralized systems, software science, user interfacing.
More about Philips Research, you will find here: https://www.philips.com/a-w/research/home

Required Skills and Experience

• Experience with using a broad range of Amazon Web Services technologies to develop and maintain secure AWS (Amazon Web Services) based cloud solutions.
• Experience in building large-scale machine-learning infrastructure
• Experience building CI/CD Pipelines for ML Algorithms, automating the end2end process for ML Pipelines
• Experience with Big Data technologies such as Hadoop, Spark, Pig, Hive
• Hands on experience with AI frameworks (TensorFlow, PyTorch, etc.).
• Experience with container technologies (e.g., Docker and/or Kubernetes) 
• Deep knowledge and hands on cloud experience in relation to AI (e.g., AWS SageMaker, Google Vertex AI Platform, etc.)
• Relevant AI/ML knowledge gained via formal education and/or in combination with industrial experience
• Strong computer science fundamentals in design, data structures, algorithm design, problem solving, and complexity analysis
• Demonstrable software craftsmanship and professional experience in software development
• Demonstrable hands-on experience in at least one modern programming language such as Python, Java, Scala, Go
• Ability to explain, communicate and influence broad audience (from highly technical to managerial) subjects like machine learning concepts, system architecture and trial results?
• Experience in mentoring junior AI/ML engineers to improve their skills, and make them more effective
• Proven abilities to converge initial questions into concrete scientific target and solution proposals.
   

Other Candidate Requirements: 

• Professional commitment to high quality, and a passion for learning new skills
• Curios and detail-oriented individual with the ability to learn rapidly new concepts and technologies
• Strong problem-solving skills, including providing simple solutions to complex situations
• Strong communicator and open for discussion with positive and assertive attitude
• Must be a strong team player with the ability to communicate and collaborate effectively in a geographically dispersed working environment