Lead ML Ops Engineer (Hybrid)
Globe Life Inc.USA, Texas, McKinneyjob_description.job_card.30_days_ago
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job_description.job_card.job_descriptionExtensive hands-on experience including design and implementation across broad range of database services on Amazon Web Services (AWS). Solid understanding of various Data Management and Data Pipeline tools available in AWS. Strong programming knowledge and experience with Python. Working knowledge with primary AWS Services like S3, Lambda, Batch, Glue, Athena, EC2, EBS, CloudWatch, CloudTrail, ECS, ECR, EMR, IAM, SNS etc. Experience with Airflow, and with the Amazon Managed Airflow administration and configurations. Experience in creating and deploying CloudFormation Templates (CFTs) Experience with Lifecycle Management of S3 Buckets. Clear Understanding of Cloud Databases Security involving AWS IAM Users and access, IAM Roles and Policies, Federated Users and permissions. Good Understanding of AWS Encryption methodologies and AWS KMS Services. Experience with database performance testing and capacity planning. Working knowledge and experience with software development life cycle (SDLC) and agile / iterative methodologies. Knowledge of AWS Machine Learning Offerings will be a plus Application development and deployment experience with Azure and AWS, CI / CD pipelines, Git, and related tech. Reliable and predictable attendance of your assigned shift Ability to work full time and / or part time based on the position specifications. Bachelor's degree in Computer Science / Engineering, Information Systems or equivalent work experience in a technical position 5+ years of experience in software development and cloud technologies. 3+ years of experience in Software Engineering primarily using Python, and AWS architectures Proven experience in building cloud applications and deploying with understanding of CI / CD processes Strong coding and scripting experience with Python, C#, SQL, or similar languages. Experience of implementing Airflow in AWS (MWAA) will be a plus Prior domain experience of Life Insurance, Annuity or Financial Services is a plus AWS Certifications are considered a strong plus Excellent verbal and written communication skills. Ability to work independently and as part of team.
Job Description :
Lead ML Ops Engineer (Hybrid)
Primary Duties & Responsibilities
Job Summary
Experience what being part of the Globe Life family feels like. Be inspired by your leaders, encouraged, and cheered on by your teammates to excel and be supported in your career while working with us. We offer a competitive salary with a great benefits package, including 401(K) match, medical, dental, and vision health plans, short - term and long-term disability, paid time off, tuition reimbursement and other career development opportunities.
The Lead ML (Machine Language) Ops Engineer will support our Cloud Applications, and drive cloud best practices for application development. This role will support a wide range of applications and solutions supporting multiple teams in the organization. In this role, an employee will work with data engineering, data analysts, business stakeholders and data scientist team(s) to help the organization build out secure, scalable, fault-tolerant and high performing cloud-based application services.
PRIMARY DUTIES & RESPONSIBILITIES
- Design, implement, and support various applications and services on our Cloud Platform.
- Provide technological guidance on Data Lake and Enterprise Data Warehouse design, development, implementation and monitoring.
- Understand, Design and Implement Data Security around cloud infrastructures.
- Provide support and guidance to Data Services and other application development teams on various AWS Database Products.
- Work with leadership on process improvement and strategic initiatives on Cloud Platform.
Required Skills
REQUIRED SKILLS
Applicable to all employees of Globe Life & Accident and its subsidiaries :
Required Knowledge & Experience
REQUIRED EXPERIENCE
Location : 3700 S. Stonebridge Dr., McKinney, Texas
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Ml Ops Engineer • USA, Texas, McKinney
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