Principal Artificial Intelligence/Machine Learning Engineer - MLOps

Job Description :

We are looking for a highly experienced and visionary Principal AI/ML Engineer to lead the design, development, and deployment of cutting-edge data science and machine learning solutions within the Data Science and ML Capability Center (CC) of our Information, Data, and Analytics (IDA) team.

This role is ideal for someone who thrives in a fast-paced, collaborative environment and has a passion for building robust, scalable, and compliant ML systems that drive real business impact.

Roles and Responsibilities :

- Lead the end-to-end design, development, and deployment of machine learning solutions and applications across multiple business lines.

- Mentor and manage a small team of AI/ML Engineers, fostering innovation and high performance.

- Drive the implementation of MLOps practices for operationalizing ML models with automated monitoring, retraining, and deployment pipelines.

- Build and maintain robust infrastructure for feature engineering, model training, deployment, and ongoing performance monitoring.

- Collaborate closely with data scientists, data engineers, product managers, and DevOps teams to enhance model performance and deployment velocity.

- Translate business requirements into scalable, reusable ML architectures and data pipelines.

- Continuously improve the AI/ML workflows, leveraging automation tools and emerging technologies to streamline the lifecycle from experimentation to production.

- Ensure all ML solutions adhere to Shells enterprise-grade compliance, security, and quality standards.

- Contribute to organizational knowledge-sharing and community-building initiatives like Centers of Excellence (CoEs) and Communities of Practice (CoPs).

- Stay up to date with the latest advancements in ML, MLOps, and data engineering technologies, tools, and best practices.

Preferred Qualifications :

- Experience in Cloud Platforms like AWS, Azure, GCP

- Experience in Languages like Python, JavaScript (JS)

- Good knowledge of Tools/Frameworks like Kubernetes, Kubeflow, MLflow, Airflow, AutoML frameworks

- Understanding of CI/CD pipelines, version control, monitoring tools, model registries

- Masters or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field.

- Proven track record of delivering production-grade ML systems in complex enterprise environments.

- Excellent communication and leadership skills.

- Experience working in agile, cross-functional teams