Summary
Our talented Data & AI Practice is made up of globally recognized experts - and there's room for more analytical and ambitious data professionals. If you're passionate about helping clients make better data-driven decisions to tackle their most complex business issues, let's talk. Take your skills to a new level and launch a career where you can truly do what matters.
As an AI Engineer within our Data & AI Practice, you'll leverage a client-centric approach to translate our client's business strategy into solutions and services that lead to successful, impactful outcomes.You'll engage in solutioning and support on the most strategic opportunities, intervene in high-risk complex deliveries, and contribute toshape the strategic direction around data architecture.
Together we do what matters.
Key Responsibilities
- As an AI Engineer, you will collaborate with cross-functional teams to design, develop, and deploy AI/ML solutions that address real-world business challenges.
- Build, train, and optimize machine learning models, including supervised, unsupervised, and deep learning approaches.
- Implement and maintain data pipelines, ensuring data quality and accessibility for model development and production.
- Work with cloud platforms (such as Azure, AWS, or GCP) to operationalize AI solutions, leveraging services like Azure ML, Cognitive Services, or their equivalents.
- Participate in code reviews, contribute to technical documentation, and support the integration of AI models into production systems.
- Stay current with the latest AI/ML research, tools, and best practices, and proactively share knowledge with the team.
- Troubleshoot and resolve issues in model performance, data quality, and deployment pipelines.
- Communicate technical concepts and results to both technical and non-technical stakeholders.
Qualifications
Skills and experiences
- 46 years of hands-on experience in AI/ML engineering, including building and deploying models in production environments.
- Proficiency in Python (must have), with experience in libraries such as scikit-learn, TensorFlow, PyTorch, or similar frameworks.
- Solid understanding of machine learning algorithms, model evaluation, and feature engineering.
- Experience with cloud AI/ML services (Azure ML, AWS SageMaker, GCP AI Platform) and containerization (Docker, Kubernetes) is highly regarded.
- Familiarity with MLOps practices, version control (Git), and CI/CD pipelines for model deployment.
- Working knowledge of data engineering concepts, including ETL processes and data pipeline orchestration.
- Ability to analyze large datasets, perform data wrangling, and visualize results using tools like Power BI or matplotlib.
- Strong problem-solving skills, attention to detail, and a passion for continuous learning.
