Senior AI Engineer

Responsibilities

• Assist our customers in identifying, evaluating, and prioritizing AI-ready use cases.
• Design, develop, and implement complex machine learning models and algorithms to solve real-world business problems.
• Collaborate with cross-functional teams, including data engineers, analysts, and subject matter experts, to understand business requirements and translate them into data science solutions.
• Lead client-facing project teams of AHEAD data science and AI experts.
• Develop and train custom models using frameworks such as Scikit-Learn, TensorFlow or PyTorch.
• Leverage Jupyter-based environments for exploratory data analysis, model development, and deployment.
• Communicate complex technical concepts and findings to stakeholders and leadership in a clear and concise manner.
• Mentor and provide guidance to junior data scientists.
• Participate in pre-sales activities, including project scoping, estimation, and solution design.
• Educate clients and stakeholders on data science and machine learning concepts, methodologies, and best practices.
• Contribute to the development and enhancement of AHEAD's data science service offerings and thought leadership.

Qualifications

• Bachelor’s or master’s degree in computer science, Statistics, Mathematics, or a related quantitative field.
• Minimum of 5 years of experience in a data science-related role, with a focus on machine learning and deep learning.
• Strong coding skills with an emphasis on writing efficient, scalable, and maintainable code.
• Experience with developing and training custom deep learning models using TensorFlow, PyTorch, or scikit-learn.
• Hands-on experience with Jupyter Notebooks, Azure Machine Learning Studio, Azure OpenAI, AWS SageMaker.
• Hands-on experience with Large Language Models (LLMs) such as LLama, Anthropic's Claude, Bedrock and familiarity with OpenAI's GPT models.
• Solid understanding of transformer architectures, attention mechanisms, and other advanced deep learning concepts.
• Knowledge of generative AI concepts, including fine-tuning, transfer learning, and RAG methods.
• Experience with the machine learning lifecycle, including model deployment, monitoring, drift detection/retraining, and canary testing.
• Strong communication and collaboration skills, with the ability to present complex technical information to both technical and non-technical audiences.
• Experience in pre-sales activities, including project scoping, estimation, and solution design.

Nice to Have

• Experience training and deploying models on DataRobot or Dataiku platforms.
• Familiarity with data platform architectures, database design, and BI platform such as PowerBI.
• Knowledge of identifying and applying non-predictive or generative methods to business problems, including ad-hoc analysis, linear programming (optimization), or designing and running simulations.
• Wide knowledge of the current state of ML and AI technology and market.
• Experience writing AI and ML blogs and go to market materials.
• Certifications: Mentioning any relevant certifications (e.g., AWS Certified Machine Learning Specialty, TensorFlow Developer Certificate) could further demonstrate technical depth.
• Open Source Contributions: Highlighting experience contributing to open-source projects, especially in the machine learning and deep learning communities, can showcase expertise and active engagement with the latest technologies.