Senior Technology Spec

Job Overview:

The Senior Technology Specialist (IT) applies in-depth knowledge, experience, and best practices in the area of technology. They are responsible for solving complex technical problems and work independently with minimal guidance.

Essential Qualifications and Education:

• Bachelor, Master s or PhD Degree in Computer Science, Engineering, AI, Mathematics, or a related field.
• 5-10 yrs in AI/ ML or software development, with a proven track record of successful projects.
• Strong working knowledge of many design techniques and analysis methods, and detailed knowledge of the content and application of standards, codes and guidelines as applicable.
• Extensive experience in AI and ML, with a strong portfolio of successful projects.
• Expert-level knowledge in programming languages such as Python, Java, or other relevant programming languages.
• Deep expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, pandas).
• Solid understanding of data structures, algorithms, statistical methods, and computational complexity.
• Published research or significant contributions to the AI/ML field is preferred.
• Experience with cloud platforms like AWS, Azure, or Google Cloud.
• Familiarity with DevOps practices and tools.
• Exceptional problem-solving, analytical, and critical thinking skills.
• Outstanding communication, leadership, and project management abilities
• Show flexibility and ensure proper hand-over with regards to:
  • The reassignment to other departments/construction sites/fabrication yards.
  • The replacement of colleagues during illness and holidays.
  • The provision of assistance to other colleagues with heavy workload (also other projects) when possible/desirable.
  • The managing/learning of current working methods and software applications.
  • The reassignment to other McDermott offices

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Key Tasks and Responsibilities:

• Perform product development from idea to prototype and further deployment.
• Apply knowledge and skills to a wide range of standard and nonstandard situations.
• Manage own time to meet agreed deadlines and budgets and develop plans for short-term work activities in own area.
• Clearly communicate and explain complex issues and work to establish understanding.
• When acting as Product Lead, in addition:
  • Direct small or medium Engineering team as a Product Lead Engineer.
  • Lead the assigned work area and complete within planned schedule and budget, in accordance with standards, MDR and project - specific procedures and to a high professional standard.
  • Plan, organize and direct all aspects of Discipline execution on the assigned project including scope, deliverables, schedule and all worker resources - agree allocations with the Discipline Manager.
  • Ensure interfaces and deliverables are clearly identified.
  • Maintain responsibility for progress and productivity, identifying any required corrective action.
• Act as project representative for the Discipline during meetings with Project Team, Customer discipline lead and relevant agencies such as certifying authorities, auditors, third parties etc.
• Strategic Development and Leadership: Develop and implement a comprehensive AI/ML strategy aligned with organizational goals. Lead teams in the design, development, and deployment of advanced AI/ML systems.
• Advanced Model Engineering: Design and engineer state-of-the-art machine learning models. Address complex challenges in AI, such as scalability, efficiency, and automation.
• Technical Oversight: Provide technical leadership and oversight across multiple projects. Ensure best practices are followed and that solutions are scalable, sustainable, and effectively integrated.
• Research and Innovation: Drive research initiatives to explore new technologies and methodologies in AI/ML. Stay abreast of industry trends and advancements to maintain a competitive edge.
• Mentorship and Team Development: Mentor and develop junior and mid-level engineers and programmers. Foster a culture of learning and continuous improvement within the team.
• Stakeholder Engagement: Engage with stakeholders across the organization to understand business needs and translate them into AI/ML solutions. Communicate complex technical concepts to non-technical audiences.
• Solution Architecture: Architect and design robust AI/ML systems and frameworks. Ensure systems are scalable, reliable, and efficient.
• Quality Assurance and Best Practices: Establish and maintain high standards of quality. Implement best practices in coding, testing, and deployment.
• Integrate models into systems:
  • Collaborate with software engineers and IT teams to seamlessly incorporate machine learning models into production environments.
  • Design and develop APIs or interfaces for model integration, ensuring compatibility with existing systems.
  • Optimize model performance for real-time or batch processing as required by the application.
  • Develop automated workflows for model training, testing, and deployment to streamline the integration process.