Role Description
As an AI Solution Engineer, you will play a pivotal role in supporting our AI Delivery Team, focusing on the packaging, deployment, and seamless integration of AI applications into existing business and IT processes. This position requires a technical expert who is passionate about AI, innovative solution design, and ensuring that complex AI systems operate reliably and at scale. The selected candidate will help define and implement best practices, drive technical excellence, and ensure the robustness and scalability of our AI offerings.
Key Responsibilities
1. AI Application Packaging: Collaborate with data scientists and our to package AI models and applications into deployable artifacts, ensuring that all dependencies, configurations, and supporting files are included for robust deployment.
2. Support deployment of AI Solutions: Provide technical support & advice for the deployment of AI models and solutions across various environments (development, staging, production), leveraging containerization technologies, orchestration frameworks, and cloud platforms as needed.
3. Best Practice Implementation: Develop and enforce best practices for AI model management, deployment, versioning, rollback strategies, and continuous integration/continuous deployment (CI/CD) pipelines.
4. Continuous Improvement: Stay informed about the latest industry trends, technologies, and methodologies in AI engineering, proposing and implementing improvements to current processes and solutions.
Required Qualifications
5. Proven experience (3+ years) in AI/ML engineering, software development, or DevOps with a strong focus on deploying and integrating machine learning applications.
6. Prior exposure to high-availability systems and scaling distributed AI applications.
7. Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related technical field.
Technical Skills
8. Strong expertise in Python
9. Hands-on experience with Azure Cloud, Azure Data & AI services, and containerization ( Docker, Kubernetes)
10. Familiarity with production-ready Data/AI product creation
11. Strong understanding of machine learning lifecycle management, model versioning, and reproducibility.
12. Experience with MLOps tools and frameworks ( MLflow, Kubeflow, TFX).
13. Expertise with CI/CD pipelines, version control (Git), and modern software development best practices.
14. Knowledge of monitoring, logging, and alerting solutions for AI applications.
15. Understanding of security and compliance requirements in AI/ML deployment contexts is a big plus
16. Familiarity with another cloud platform (GCP or AWS) is a big plus
17. Familiarity with data pipeline design and ETL processes is a plus
Soft Skills
18. Excellent problem-solving skills.
19. Collaboration: Strong interpersonal skills to work effectively with diverse stakeholders from data science, engineering, and business units.
20. Excellence: Strong commitment to excellence by consistently striving for high-quality outcomes, embracing continuous improvement, and maintaining a proactive, detail-oriented approach in all tasks.
21. Innovation Mindset: Willingness to explore new technologies and methodologies to drive continuous improvement.
22. Resilience: Capacity to troubleshoot and resolve issues in complex changing environments while maintaining a focus on quality and user experience.
23. Communication: Ability to convey complex technical concepts to non-technical audiences and provide clear documentation.
What We Offer
24. Opportunity to work at the forefront of AI technology and make a tangible impact on enterprise transformation.
25. Collaborative, inclusive, and dynamic work environment.
26. Competitive compensation, benefits, and opportunities for professional growth.
27. Flexibility in working arrangements, with remote and hybrid options.