AI Engineer Manager | Hybrid

About the Job

As an AI Engineer Manager, you will lead multidisciplinary teams in the design, development, and deployment of data-driven solutions, transforming raw data into actionable insights that drive business growth. Within our Advisory practice, you will leverage advanced analytics and statistical techniques to extract insights from large datasets, enabling informed decision-making and solving complex business problems.

As a Manager, you will enhance your leadership style by motivating and developing your team to deliver quality results. You will be responsible for coaching and leveraging team members' unique strengths to meet client expectations. With your growing business knowledge, you will play a crucial role in identifying opportunities that contribute to the success of our firm. You are expected to lead with integrity and authenticity, articulating our purpose and values meaningfully.

Responsibilities

Leading data science initiatives to transform raw data into actionable insights for informed decision-making Guiding teams in the development and deployment of advanced data solutions using machine learning and artificial intelligence Utilizing statistical modeling and predictive analytics to address complex business challenges Designing and implementing data pipelines and data lakes to support large-scale data processing Overseeing the creation of data visualizations to communicate insights effectively to stakeholders Managing project timelines and budgets to deliver data-driven solutions on schedule Coaching team members to enhance their skills in data science and analytics Collaborating with business leaders to identify opportunities for leveraging data to drive growth Promoting the adoption of innovative technologies and methodologies in data engineering and analytics Confirming data quality and compliance within analytics frameworks to maintain integrity

Must Have

At least a Bachelor's degree At least 7 to 11 years of experience Oral and written proficiency in English required Strong full-stack or backend development skills, including Python, Angular or React, RESTful APIs, microservices, SQL/NoSQL databases, testing, and software architecture. Hands-on experience building AI-integrated applications, using LLMs, RAG, embeddings, vector databases, and frameworks such as LangChain, LlamaIndex, or Autogen. Proven cloud and DevOps capability, including Azure, AWS, or GCP deployment, Docker, Kubernetes, CI/CD, Git, security, reliability, and scalability.

What Sets You Apart

Preference for at least one of the following fields of study: Artificial Intelligence and Robotics, Business Analytics, Computer and Information Science, Computer Engineering, Computer Programming, Data Processing/Analytics/Science, Engineering, Information Technology, Machine Learning, Management Information Systems, Mathematics, Statistics, Systems Engineering Demonstrating proficiency in data science algorithms and workflows Utilizing machine learning and deep learning techniques effectively Excelling in complex data analysis and predictive modeling Managing data pipelines and confirming data quality Leveraging programming languages such as Python, R, and MATLAB Embracing change and innovation in technology-driven environments