AI ML Senior Engineer

• Help design, build and continuously improve the clients online platform.
  


• Research, suggest and implement new technology solutions following best practices/standards.
  


• Take responsibility for the resiliency and availability of different products.
  


• Be a productive member of the team.
  

Requirements

• AI/ML Senior Engineer to join our growing ML Engineering team.
  


• Collaborate closely with data scientists, engineers, and product managers to design, deploy, and operate production\-grade machine learning systems that power critical services across our Digital and Retail platforms.
  


• This position has a strong focus on MLOps and ML platform development, helping scale and maintain reliable, end\-to\-end ML workflows using modern cloud\-native infrastructure and tools.
  


• Design, build, and maintain ML pipelines for model training, validation, deployment, and monitoring.
  


• Enable scalable ML solutions for use cases such as recommendation systems, forecasting, and intelligent automation.
  


• Develop and deploy production\-ready services using tools such as Airflow, Azure ML, and FastAPI.
  


• Automate model build and deployment workflows using CI/CD pipelines (GitHub Actions, Azure DevOps).
  


• Ensure reliability, observability, and performance of the ML platform.
  


• Collaborate with data scientists to productionize research models and code into scalable services.
  


• Implement monitoring, alerting, and model drift detection using tools like Azure Monitor, New Relic, Grafana, and custom logging frameworks.
  


• Continuously improve and manage cloud infrastructure using Terraform, Docker, and Fargate.
  


• Proven experience in ML Engineering, MLOps, DevOps, or Data Engineering with exposure to the full ML lifecycle.
  


• Hands\-on experience building and maintaining ML workflows and pipelines.
  


• Strong proficiency in Python, with experience using MLflow, Scikit\-learn, or PyTorch.
  


• Experience with cloud platforms, particularly Azure and/or AWS.
  


• Solid understanding of containerization (Docker) and orchestration technologies such as Kubernetes.
  


• Hands\-on exposure to CI/CD tools (GitHub Actions, Azure DevOps) and Infrastructure as Code (Terraform).
  


• Strong collaboration and communication skills, with the ability to work in cross\-functional teams.
  


• Languages: Python (primary), SQL, Bash
  


• Cloud Platforms: Azure, AWS
  


• ML & Workflow Tools: MLflow, Azure ML, Airflow
  


• APIs & Services: FastAPI, Azure Functions
  


• Data Platforms: Snowflake, Delta Lake, Redis, Azure Data Lake
  


• Infrastructure & DevOps: Docker, Fargate, Terraform, GitHub Actions, Azure DevOps
  


• Monitoring & Observability: Grafana, Azure Monitor, New Relic
  


• Experience working with enterprise data platforms such as Snowflake or Azure Data Lake.
  


• Experience deploying ML models as APIs or microservices.
  


• Strong understanding of model performance tracking, monitoring, and observability best practices.
  


• Familiarity with orchestration tools such as Airflow or Azure Data Factory.
  

Benefits

• A challenging, innovating environment.
  


• Opportunities for learning where needed.