Lead ML Ops Engineer
4 weeks ago
Lead ML Ops Engineer
Job Description:
Job Summary:
We are seeking a highly skilled Lead ML Ops Engineer to join our team. As a key member of our infrastructure team, you will be responsible for designing, developing, and maintaining the infrastructure for our machine learning applications.
Key Responsibilities:
- Develop automated build and deployment processes to enable continuous delivery of software releases.
- Enhance the existing CI/CD pipelines for AIML application development and deployment.
- Collaborate with data scientists, data engineers, data analysts, software engineers, IT specialists, and stakeholders to accelerate deployment of AI applications via CI/CD pipelines and maintain the SLAs of those applications at the centralized platform.
- Design, develop, and maintain infrastructure using infrastructure as code tools such as Terraform, Ansible, CloudFormation, etc.
- Templatize existing Databricks CLI codes to manage Databricks platform as code for AIML data pipelines (batch processing, batch streaming, and streaming) and model serving endpoints.
- Enhance the existing DevOps practices to improve the overall AIML application development lifecycle.
- Work closely with cross-functional teams to ensure that applications are highly available and scalable.
- Collaborate with development teams and cloud platform team to ensure that infrastructure meets the requirements of the application.
- Establish and maintain best practices for cloud security, compliance, and cost optimization.
Requirements:
- Bachelor's Degree in Computer Science, Computer Engineering, Information Technology, Software Engineering, or equivalent technical discipline.
- 10+ years of experience in software engineering with a strong background in DevOps and Infrastructure as Code, supporting Machine Learning and Data Science workloads preferred.
- Expertise on code versioning tools, such as Gitlab, GitHub, Azure DevOps, Bitbucket, etc.
- Experience deploying Machine Learning solutions on cloud platforms (e.g., AWS, Azure, or GCP).
- Proficient with GitHub actions to automate testing and deployment of data and ML workloads from CI/CD provider to Databricks.
- Strong knowledge of infrastructure automation tools such as Terraform, Ansible, CloudFormation, etc.
- Experience with data processing frameworks/tools/platform such as Databricks, Apache Spark, Kafka, Flink, AWS cloud services for batch processing, batch streaming, and streaming.
- Experience containerizing analytical models using Docker and Kubernetes or other container orchestration platforms.
- Technical expertise across all deployment models on public cloud, private cloud, and on-premises infrastructure.
- Experience in event-driven, and microservice architectures for enterprise-level platform development.
- Expertise in Linux, and knowledge of networking and security concepts.
- Effective communication skills and a sense of ownership and drive.
- Capable of coaching/mentoring individuals and teams.
About ManpowerGroup:
ManpowerGroup is a leading global workforce solutions company that helps organizations transform in a fast-changing world of work by sourcing, assessing, developing, and managing the talent that enables them to win.
We develop innovative solutions for hundreds of thousands of organizations every year, providing them with skilled talent while finding meaningful, sustainable employment for millions of people across a wide range of industries and skills.
Our expert family of brands – Manpower, Experis, Talent Solutions, and Jefferson Wells – creates substantial value for candidates and clients across more than 75 countries and territories and has done so for over 70 years.
We are recognized consistently for our diversity – as a best place to work for Women, Inclusion, Equality, and Disability – and in 2022 ManpowerGroup was named one of the World's Most Ethical Companies for the 13th year, confirming our position as the brand of choice for in-demand talent.
ManpowerGroup is committed to providing equal employment opportunities in a professional, high-quality work environment.
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