Innovation Technology Specialist

Innovation and Technology Specialist will play a pivotal role in driving innovation across the company by identifying emerging technologies, exploring their potential applications, and championing the development of commercially viable solutions. This role requires a deep understanding of AI/ML technologies, strong problem-solving skills, and the ability to lead and mentor a team of data scientists and engineers.

Innovation and Technology Specialist will design and implement AI/ML solutions that address complex business challenges. You will collaborate closely with cross-functional teams to define and execute AI/ML strategies, develop robust algorithms, and ensure the successful deployment of AI/ML models into production environments. This role requires a deep understanding of AI/ML technologies, strong problem-solving skills, and the ability to lead and mentor a team of data scientists and engineers.

Responsibilities:

• Collaborate with stakeholders to identify business problems that can be solved using AI/ML techniques.
  
• Stay current with the latest advancements in AI/ML and recommend innovative approaches to solving problems.
  
• Lead the development and implementation of AI/ML solutions, from concept to production deployment.
  
• Design and architect scalable and efficient AI/ML systems and pipelines.
  
• Provide technical leadership and mentorship to data scientists and engineers.
  
• Collaborate with data engineers to ensure data pipelines are optimized for AI/ML applications.
  
• Evaluate and select appropriate AI/ML frameworks, tools, and technologies.
  
• Ensure the ethical and responsible use of AI/ML by implementing fair and transparent algorithms.
  

Requirements:

• Degree / Masters / PhD in Computer Science, Data Science or related field. Chemical, Instrumentation Engineering background with software decelopment and AI engineering could also be considered
  
• Minimum 5 years' experience in AI architecture and pipeline planning. Understand the workflow and pipeline architectures of ML and deep learning workloads. An in-depth knowledge of components and architectural trade-offs involved across the data management, governance, model building, deployment, and production workflows of AI is a must.
  
• Minimum 5 years' experience in Software engineering and DevOps principles,including knowledge of DevOps, ML Ops workflows, and tools, such as Git, containers, Kubernetes, Kube-flow, and CI/CD.
  
• Minimum 5 years' experience in Data science and advanced analytics, including knowledge of advanced analytics tools (such as SAS, R, and Python) along with applied mathematics, ML, and Deep Learning frameworks (such as TensorFlow, Scikit-learn, Keras) and ML techniques (such as AutoML, random forest and neural networks).
  
• Experience in Software development for Industrial Automation and knowledge of IA protocols (OPC, OPC UA) and systems (DCS, SCADA) will be an added advantage.
  
• Minimum 10 years' experience in Software Development using Java,.NET C#, C++
  

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