AI/ML Ops Engineer at Southern Company

Atlanta, Georgia, United States -

Full Time

Start Date

Immediate

Expiry Date

06 Feb, 26

Salary

0.0

Posted On

08 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

MLOps, Cloud Engineering, Python, Machine Learning, CI/CD, Infrastructure as Code, Data Engineering, SQL, Big Data, Azure, Databricks, Apache Spark, Kafka, Problem-solving, Communication, Agile

Industry

Utilities

Description

Job Description: AI/ML Ops Engineer Position Overview The AI/ML Ops Cloud Engineer will lead the development and stewardship of the enterprise-wide MLOps framework, ensuring consistent standards, lifecycle governance, and monitoring across all AI/ML initiatives. This role will also contribute to the design, delivery, and support of reusable AI products that can be scaled across operating companies, enabling faster innovation and operational efficiency. Key Responsibilities Enterprise MLOps Ownership Architect and own the MLOps framework for the enterprise, including lifecycle management, governance, and operational standards (Azure and Databricks) Define and enforce best practices for model deployment, monitoring, and retraining across business units Establish enterprise-wide observability for AI/ML models, including performance tracking, data drift detection, and alerting Cloud Infrastructure & Automation Build and manage scalable, secure cloud-native infrastructure (Azure) for AI/ML workloads Automate deployment pipelines using CI/CD tools and Infrastructure as Code (IaC) Ensure high availability, fault tolerance, and compliance across environments Reusable AI Product Enablement Collaborate with cross-operating company teams to design reusable AI components and services Support delivery and lifecycle management of AI products across operating companies Develop shared templates, APIs, and deployment patterns to accelerate adoption Security, Compliance & Collaboration Implement enterprise-grade security and compliance controls for AI/ML systems Partner with data engineering, DevOps, and business stakeholders to align infrastructure with strategic goals Provide technical leadership and mentorship to teams adopting MLOps practices Qualifications Educational Background: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field. Experience: Proven experience (5+ years) in cloud engineering or DevOps with 2+ years in MLOps or AI infrastructure, Data Engineering, ML Engineering, or similar role. Technical Skills: Deep expertise in Python. Experience with machine learning frameworks and libraries like PyTorch, or scikit-learn. Experience with ML lifecycle tools like MLflow. Cloud Platforms: Experience with cloud computing services (Azure preferred) and their machine learning tools. Big Data Technologies: Familiarity with big data processing tools such as Databricks, Apache Spark, and Kafka. Familiarity with data integration and scalable pipeline design. Experience with SQL and understanding of relational and NoSQL databases. Analytical Mindset: Excellent problem-solving skills and the ability to analyze complex data sets. Communication: Strong verbal and written communication skills, with the ability to present complex technical concepts to non-technical stakeholders. Preferred Qualifications Certifications: Relevant certifications in AI, ML, or data engineering. Industry Experience: Experience in the energy sector is a plus. Experience building low-code solutions using Microsoft Power Apps, especially for operational workflows or AI product interfaces Experience designing reusable AI products or services in a multi-business environment Project Management: Experience with Agile methodologies and project management tools. What We Offer Competitive salary and benefits package Opportunities for professional growth and development Collaborative and inclusive work environment Access to cutting-edge technologies and resources

Responsibilities

The AI/ML Ops Engineer will lead the development of the enterprise-wide MLOps framework, ensuring governance and monitoring across AI/ML initiatives. This role also involves collaborating with teams to design reusable AI products for operational efficiency.