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Mid-level ML Engineer with popular GenAI and MLOps skills yields moderate applicant competition.
Technical ML/MLOps skills transfer across industries but require production-grade and GenAI experience, so moderate sensitivity.
Multiple mandatory MLOps, cloud, Spark, Kubernetes, and 5+ years requirement increases filtering strictness.
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Design, develop, deploy, and maintain scalable machine learning models and pipelines including data ingestion, feature engineering, training, validation, deployment, and monitoring.
Implement MLOps best practices covering experiment tracking, model versioning, CI/CD, model governance, automated retraining, and production model monitoring including data drift detection.
Build and deploy GenAI applications using LLMs, RAG frameworks, vector databases, and collaborate with DevOps on cloud infrastructure optimization and real-time inference endpoints.
Minimum 5 years of experience in Machine Learning Engineering, Data Science, MLOps, or Data Engineering.
Proficient in Python programming and experienced with Spark/PySpark and distributed data processing.
Experience with MLOps platforms (MLflow, Azure ML, Databricks), containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, and cloud ecosystems (Azure, AWS, or GCP).
Experience deploying Generative AI use cases involving prompt engineering and RAG Framework in production.
Experienced with end-to-end ML lifecycle management in production environments, skilled at operationalizing models with MLOps frameworks and governance.
Technically adept with distributed processing and cloud-native technologies, capable of collaboration with data science, engineering, and DevOps teams.
Strong in deploying advanced GenAI solutions, including LLMs and RAG-based applications, bringing both software engineering and machine learning expertise at senior level.