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Mid-level ML engineering role with common skills and moderate brand, attracting many qualified applicants.
Skills like ML engineering, MLOps, and cloud are highly transferable across industries.
Explicit 5+ years requirement plus mandatory MLOps, cloud, Spark, and Kubernetes skills increases shortlisting strictness.
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Design, develop, and deploy scalable machine learning models and pipelines covering all stages from data ingestion to monitoring.
Implement MLOps best practices including experiment tracking, model versioning, CI/CD, automated retraining, and governance for ML workloads.
Develop and deploy Generative AI applications using LLMs, RAG frameworks, vector databases, and prompt engineering, ensuring production reliability and scalability.
5+ years of experience in Machine Learning Engineering, Data Science, MLOps, or Data Engineering.
Proficiency in Python programming and experience with distributed data processing frameworks such as Spark/PySpark.
Experience with MLOps platforms (e.g., MLflow, Azure ML, Databricks) and CI/CD pipelines including Docker and Kubernetes.
Bachelor’s or master’s degree in Computer Science, Engineering, or a related field.
Demonstrated ability to operationalize and monitor production ML models including handling model governance and data drift.
Experience deploying Generative AI solutions involving prompt engineering and RAG frameworks in cloud environments (Azure, AWS, or GCP).
Skilled collaborator with data scientists, data engineers, and DevOps teams to optimize cloud infrastructure and deployment processes.