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Mid-level Bangalore MLOps role with broad skillset and multiple in-demand requirements.
Strong ML and cloud bias makes skills transferable but requires specialized MLOps experience.
Explicit 5–8 years, mandatory MLOps, cloud, Kubernetes, and model deployment skills.
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Design, develop, and deploy scalable machine learning models and pipelines including data ingestion, feature engineering, training, validation, deployment, and monitoring.
Implement and maintain MLOps best practices such as experiment tracking, model versioning, CI/CD, model governance, and automated retraining in production environments.
Develop and deploy Generative AI applications leveraging LLMs, RAG frameworks, vector databases, and prompt engineering while optimizing cloud infrastructure and security.
5+ years of experience in Machine Learning Engineering, Data Science, MLOps, or Data Engineering.
Proficiency with MLOps platforms (e.g., MLflow, Azure ML, Databricks), CI/CD pipelines, containerization (Docker), and orchestration platforms (Kubernetes).
Hands-on experience with Spark/PySpark, distributed data processing, and cloud platforms (Azure, AWS, or GCP).
Experience deploying production Generative AI use cases involving prompt engineering and RAG frameworks.
Experienced in operationalizing machine learning solutions collaboratively with Data Scientists and Data Engineers in cloud environments.
Skilled in building and optimizing real-time and batch inference services using streaming frameworks like Kafka.
Proficient in implementing advanced MLOps workflows and governance including model monitoring, data drift detection, and AI explainability.