Senior AI

Job Description :

• We're looking for an AI & ML Senior Engineer to join our growing ML Engineering team. You'll work alongside data scientists, engineers, and product managers to develop, deploy, and operate robust ML systems that power key services across our Digital and Retail platforms.
• This role focuses on ML Ops and ML Platform Engineering and development, helping us to scale and maintain production-ready ML workflows using modern cloud infrastructure and tools.
• Knowledge of feature stores, model registries, and enterprise ML governance.
• Multi-cloud AI/ML experience (AWS/GCP in addition to Azure).
• Contributions to open-source MLOps/GenAI frameworks.

Key Responsibilities:

• Develop and maintain ML pipelines for training, validation, deployment, and monitoring of models.
• Support scalable ML solutions across use cases like recommendations, forecasting, and automation.
• Work with tools like Airflow, Azure ML, and FastAPI to deliver production-ready systems.
• Automate model deployment workflows using CI/CD tools (e.g. GitHub Actions, Azure DevOps).
• Contribute to the reliability, observability, and performance of the ML platform.
• Collaborate with data scientists to productionise research code and models into scalable services.
• Help implement monitoring, alerting, and model drift detection using tools like NewRelic, Azure Monitor, and custom logging frameworks.
• Continuously improve and manage infrastructure using Terraform, Docker, and Fargate.
• MLOps / GenAI Ops
• Design and implement end-to-end ML pipelines (data ingestion, training, validation, deployment, monitoring, retraining).
• Operationalize GenAI models (LLMs, embeddings, vector databases, prompt orchestration) for enterprise use cases.
• Automate CI/CD workflows for ML/GenAI apps ensuring reproducibility, scalability, and governance.
• Implement model monitoring, observability, and drift detection frameworks.
• Define best practices for ML/GenAI lifecycle management and compliance.
• Azure Cloud Engineering
• Build and optimize AI/ML solutions on Azure using services like Azure ML, AKS, Azure OpenAI
• Design cloud-native architectures ensuring scalability, high availability, and security.
• Implement cost-efficient infrastructure for large-scale AI workloads.
• Snowflake as a Data Platform
• Use Snowflake as the central data platform for feature engineering, data preparation, and model serving.
• Integrate Snowpark, Python UDFs, and Snowflake ML with AI/ML pipelines.
• Enable real-time and batch data pipelines with Snowflake + Azure ecosystems.
• Implement data governance, lineage, and observability in Snowflake for AI/ML workloads.
• Serve as a technical leader and mentor for ML and data engineers.
• Partner with data scientists to productionize research models on Snowflake and Azure.
• Engage with product and business stakeholders to align AI solutions with enterprise strategy.
• Drive innovation in GenAI Ops, MLOps, and data platform integration.
• Proven experience in ML Platform Engineering, DevOps, or Data Engineering with exposure to ML lifecycle.
• Hands-on experience building or maintaining ML workflows or pipelines.
• Strong Python skills and experience with tools like MLflow, Scikit-learn, or PyTorch.
• Familiarity with cloud platforms (especially Azure, AWS).
• Good understanding of containerisation (Docker) and orchestration (e.g. Kubernetes).
• Exposure to CI/CD tools (GitHub Actions, Azure DevOps) and infrastructure-as-code (Terraform).
• Collaborative team player with excellent communication skills.
• Languages: Python (primary), SQL, Bash
• Cloud: Azure, AWS
• Tools: MLflow, Azure ML, Airflow, Docker, Fargate, FastAPI
• Data: Snowflake, Delta Lake, Redis, Azure Data Lake
• Infra & Ops: Terraform, GitHub Actions, Azure DevOps, Grafana, Azure Monitor