We are expanding our team and looking for an experienced AIML DevOps Engineer with a strong background in Azure Cloud, Databricks, and DevOps methodologies to join our vibrant Data Science and AI team. This position offers an exciting opportunity to make a significant impact by driving automation, scalability, and delivering high-performance cloud-based solutions.
Role Responsibilities
Technical Leadership and Strategy:
- Spearhead the development of scalable AI/ML cloud architectures on Azure, ensuring the highest standards of reliability, security, and performance.
- Define and implement cloud infrastructure best practices, alongside DevOps strategies, to cultivate a culture focused on continuous technical improvement and excellence.
Solution Architecture and Design:
- Design and deploy Databricks workspaces and clusters to enable efficient data science and AI workloads at scale.
- Architect robust CI/CD pipelines for seamless AI/ML model deployment using tools like Jenkins, GitLab CI, or Azure DevOps, with an emphasis on automating testing and delivery.
- Optimize the deployment of containerized applications through Docker and Kubernetes, ensuring portability and scalability across environments.
Collaboration Across Teams:
- Partner with data scientists, Gen AI engineers, and software developers to enhance the development and deployment processes of AI/ML models.
- Act as a liaison between the infrastructure, data science, and software teams to align technical solutions with business needs.
- Contribute to code reviews, offer technical guidance, and mentor junior engineers within the AI & Data Science team.
Continuous Improvement and Innovation:
- Stay informed on the latest advancements in Azure, Databricks, and DevOps technologies, and integrate these innovations to optimize development and deployment workflows.
- Proactively identify opportunities to automate and streamline processes within the AI/ML pipeline.
- Lead internal workshops and knowledge-sharing sessions to promote a culture of continuous learning and ensure the team remains current with industry trends and best practices.
Required Qualifications and Skills:
- Bachelor's degree in Computer Science, Information Technology, or a related field; a Master's degree is preferred.
- 10+ years of experience in DevOps, Cloud Engineering, or a related role, with specific expertise in AI/ML workflows and data engineering.
- Extensive hands-on experience with Azure Cloud, including familiarity with Infrastructure as Code (IaC) tools like Terraform or Azure Resource Manager (ARM).
- Proven ability in managing Databricks environments for AI and data processing workloads.
- Proficiency in CI/CD tools such as Jenkins, GitLab CI, or Azure DevOps, and experience with scripting languages (Python, Bash, etc.).
- Strong experience with containerization technologies like Docker and Kubernetes for scalable deployments.
- Solid understanding of cloud security best practices and regulatory requirements.
Preferred Qualifications:
- Microsoft Azure certifications (e.g., Azure Solutions Architect, Azure DevOps Engineer Expert), Databricks certifications, or GenAI certifications.
- Expertise in AI/ML model lifecycle management and MLOps.
- Familiarity with Apache Spark, Delta Lake, and distributed computing environments.
- Experience with monitoring tools like Prometheus, Grafana, or Datadog.
- Knowledge of integration platforms as a service (iPaaS) such as Boomi, SnapLogic, or Workato.
This position offers a unique chance to contribute to cutting-edge AI and data solutions, making a tangible impact on the team's growth and success.
