AI Engineer

1. AI Solution Development

Collaborate with cross-functional teams to design and develop innovative machine learning models and algorithms that address specific business challenges. Implement scalable AI products, ensuring they meet user requirements and business objectives. Utilize state-of-the‑art cloud technologies (e.g., AWS, Azure, Google Cloud) to industrialize machine learning models and AI solutions for deployment in production environments. Provide operational support and guidance for machine learning models and algorithms, ensuring their reliability and performance in production settings.

Assist in data preprocessing, ingestion, and transformation activities across hybrid environments (both on-premises and cloud). Implement and maintain data quality assurance processes, including validation checks and data drift detection mechanisms, to ensure data consistency and integrity. Support the model training process, focusing on industrialization through cloud technologies and achieving accuracy, reliability through experimentation and iterative improvements. Assist in the evaluation and tuning of models, utilizing metrics and performance benchmarks to refine model parameters and enhance predictive capabilities.

Contribute to the deployment of AI solutions into production environments, following best practices for version control, architecture, monitoring, and maintenance. Prepare and present regular reports on AI project status, outcomes, and insights to stakeholders, highlighting key findings and recommendations for future work.

Required Master's or Ph.D. degree in Computer Science, Information Technology, Data Science, Artificial Intelligence or a related field or Bachelor's with minimum 5 years experience. Proven experience as an AI Engineer, Machine Learning Engineer, or similar role. Experience in developing and deploying machine learning models and AI systems. Proficiency in programming languages such as Python or R Strong knowledge of machine learning frameworks (Tensor Flow, PyTorch, Scikit-learn).

Experience with natural language processing (NLP), computer vision, or other AI techniques. Familiarity with big data technologies (Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud) Strong analytical skills and the ability to work with complex datasets. Excellent problem-solving skills with a focus on developing innovative AI solutions. Excellent verbal and written communication skills. Ability to work collaboratively in a cross-functional team environment.

Strong organizational skills and the ability to manage multiple AI projects simultaneously. Experience with Agile methodologies and tools (Azure Dev Ops, Scrum).

Deep understanding of the Azure ecosystem, specifically Azure AI, Azure Machine Learning, Compute, Storage, Networks and Azure Arc, Containers (registry, runtimes), Events, Functions, Azure Identity tooling

Microsoft Teams Bot integrations; understanding of maintaining and publishing bots, React

Relevant certifications in AI, machine learning, or data science (e.g., AWS Certified Cloud / AI practitioner, Azure Fundamentals)

Certifications:

Azure Solutions Architect or Azure AI Engineer certification