Immediate opening For Azure ML/AI Engineer

Role & responsibilities

• ML Experimentation & Lifecycle Management: Track, version, and manage experiments and models using MLflow and Azure ML Model Registry.
• Model Deployment & Optimization: Build and optimize models for low-latency inference using ONNX, TensorRT, and Azure ML endpoints.
• Monitoring & Drift Detection: Implement monitoring solutions to detect performance degradation, concept drift, and data drift in production.
• Automation & Optimization: Leverage Azure AutoML and HyperDrive for rapid prototyping, systematic model optimization, and benchmarking.
• Generative AI & LLMs: Work with Azure OpenAI, Hugging Face, or fine-tuned large language models for enterprise applications.
• Collaboration with Business Teams: Translate business challenges into AI/ML solutions and present insights to both technical and non-technical stakeholders.
• Responsible AI: Apply fairness, explainability, and governance principles to ensure ethical and compliant AI practices.
• MLOps & CI/CD: Build robust pipelines using Docker, Kubernetes (AKS), and Azure DevOps/GitHub Actions for automated deployment.

Preferred candidate profile

• 5+ years of experience in AI/ML, with 3+ years working on the Azure ecosystem (Azure ML, Databricks, Synapse, Cognitive Services).
• Strong programming skills in Python, PySpark, and SQL.
• Expertise in ML/DL frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Solid background in data preprocessing, feature engineering, and model tuning.
• Proven hands-on experience in MLOps practices, including CI/CD, Docker, Kubernetes (AKS).
• Proficiency in experiment tracking and model lifecycle management using MLflow and Azure ML.
• Experience in model deployment and inference optimization with ONNX, TensorRT, and Azure ML endpoints.
• Knowledge of automated ML and hyperparameter tuning (Azure AutoML, HyperDrive).
• Experience with Generative AI/LLMs (Azure OpenAI, Hugging Face, fine-tuning).
• Strong understanding of Responsible AI (bias detection, explainability, fairness, governance)
• Excellent communication skills with the ability to collaborate across business and technical teams.