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Remote mid-level ML role attracts many applicants, but GenAI+GCP specialization moderates density.
Specialized GenAI, LLM, MLOps and GCP requirements raise domain specificity and reduce transferability.
Explicit 5–7 years plus mandatory GenAI, GCP, and MLOps experience makes filtering strict.
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Design, build, and deploy ML applications and scalable pipelines in production with focus on Generative AI and RAG systems.
Implement and optimize models using NLP, Computer Vision, Deep Learning, and GenAI frameworks such as LangChain.
Operate and manage ML solutions on Google Cloud Platform including Vertex AI and BigQuery with MLOps best practices in a customer-facing environment.
5-7 years overall industry experience with at least 3 years hands-on ML application development in production.
3+ years experience in Generative AI including RAG, LLM prompt engineering, and orchestration tools like LangChain.
2+ years experience with Google Cloud Platform services such as Vertex AI and BigQuery for deploying ML pipelines.
Strong expertise in Python, NumPy, Pandas, Scikit-learn, machine learning frameworks TensorFlow, PyTorch, and XGBoost.
Experienced in end-to-end ML development and deployment in production-grade environments with a focus on GenAI and RAG technologies.
Comfortable working night shift with 100% remote setup across India, indicating high ownership and flexibility.
Proficient in integrating and optimizing ML pipelines on GCP with MLOps for customer-facing AI/ML products.