Technical Lead - AI & Data Science

Job Description Summary

The Technical Lead - AI & Data Science will be responsible for overseeing the development and deployment of GenAI and data science solutions in nuclear applications. This role requires deep expertise in model development, pre-training, and implementation of AI algorithms—including vision AI, predictive analytics, LLM-based applications, machine learning and data analytics. This role requires strong leadership skills to drive innovation, foster a collaborative environment, and ensure the successful delivery of AI-driven solutions.

The ideal candidate will have a proven track record in both technical and leadership domains, with a passion for leveraging AI and data science to solve complex business problems.

The Engineer will work closely with domain experts to translate operational challenges into AI-solvable problems, prototype models, validate performance, and support integration with enterprise systems. Strong Python and machine learning skills, familiarity with cloud AI platforms, and experience with MLOps pipelines are essential. This role is critical to delivering high-impact technical solutions across diverse business use cases such as factory vision systems, error analysis, and procedural automation.

• Strategy Development:
  • Develop and execute the AI and data science strategy aligned with the organization’s goals.
  • Identify opportunities for AI and data science applications across various business units.
• Leadership:
  • Lead, mentor, and grow a high-performing team of data scientists, machine learning engineers, and analysts.
  • Foster a culture of continuous learning, innovation, and collaboration.
• Project Oversight:
  • Plan and oversee the end-to-end lifecycle of AI and data science projects, from conception to deployment.
  • Ensure projects are delivered on time, within scope, and meet quality standards.
• Technical Excellence:
  • Stay abreast of the latest developments in AI, machine learning, and data science.
  • Apply best practices and emerging technologies to enhance the organization’s AI capabilities.
• Develop custom tools and utilities to enhance the generative AI pipeline, including data preprocessing scripts, model evaluation dashboards, and content visualization interfaces.
• Streamline repetitive tasks by automating processes, improving efficiency, and reducing manual intervention.
• Collaborate with QA engineers to design comprehensive test suites for the generative AI models.
• Write unit tests to validate individual components, ensuring robustness and reliability.
• Work closely with data scientists to create diverse and effective prompts for the generative models.
• Participate in prompt fine-tuning experiments to improve model performance.
• Integrate generative AI models into existing applications and services via APIs.
• Ensure seamless deployment across various platforms, including cloud environments.
• Cross-Functional Communication:
  Collaborate with data engineers, Dev Ops, and product teams to align project goals.
• Maintain clear and concise documentation for tools, APIs, and integration procedures.
• Effectively communicate complex technical concepts to both technical and non-technical stakeholders.

• Bachelor's Degree or Master’s Degree in Computer Science, Data Science, or related quantitative field with a minimum 10 years of relevant professional experience.
• 3+ years of experience in a Technical Lead or equivalent role.

• Strong proficiency in Python.
• Strong understanding of statistical modeling, machine learning algorithms (e.g., regression, classification, clustering, deep learning), and experimental design.
• Deep expertise in model development, pre-training, and implementation of AI algorithms—including vision AI, predictive analytics, LLM-based applications, and anomaly detection.
• Familiarity with cloud AI platforms, and experience with MLOps pipelines.
• Experience with SQL and working with relational and/or No
  
  SQL databases.
• Experience using modern development tooling such as Git and testing frameworks.
• General understanding of the impacts of technology choice to the software development life cycle.
• Aware of methods and practices such as Lean/Agile/XP, etc.