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Job Description
Structured overview of role & requirementsAbout This Role
Own design and architecture of scalable MLOps pipelines using Azure ML, MLflow, Databricks Asset Bundles, and CI/CD/CT.
Implement and manage machine learning models including supervised, unsupervised, reinforcement learning with strong focus on model evaluation, drift detection, and monitoring.
Ensure data governance, compliance, and facilitate mentorship through code reviews and technical knowledge sharing.
Minimum Requirements
4 to 8 years of relevant AI/ML engineering experience.
Proficiency in Python (NumPy, Pandas, scikit-learn) and experience with TensorFlow, PyTorch, or Keras for deep learning (CNNs, RNNs).
Experience with Big Data technologies such as Spark (Databricks preferred), Hadoop/Kafka, and cloud ML services on Azure, AWS, or GCP with strong MLOps exposure.
Strong SQL and NoSQL skills with experience in data modeling, plus hands-on experience with model drift detection, monitoring, and data governance frameworks.
Ideal Candidate Profile
Experienced AI/ML engineer skilled in building production-grade, scalable MLOps pipelines in cloud environments, preferably Azure.
Strong background in end-to-end machine learning lifecycle including data handling, model development, deployment, monitoring, and governance.
Capable mentor and code reviewer with interest in emerging AI fields such as Generative AI, LLMs, and Agentic AI, and able to communicate technical concepts effectively.