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Large services brand and metro location increase visibility, but senior niche MLOps role moderates applicant density.
Specialized MLOps and LLM fine-tuning experience limits transferability across non-ML industries.
Explicit 8–15 years requirement and specialized MLOps/LLM skills create strict shortlisting filters.
Own end-to-end fine-tuning and deployment of a self-hosted large language model (LLM) to replace a commercial inference pipeline for automated UI test execution.
Build and maintain training data pipelines including format conversion, deduplication, and quality filtering to ensure model quality.
Design and implement evaluation frameworks and rollout strategies (shadow-mode, A/B testing, automated rollback) to monitor and improve model performance in production.
Work experience: 8 to 15 years in AI/ML MLOps or related roles.
Location: All Persistent locations (including Pune, Maharashtra, India).
Technical expertise in supervised fine-tuning of large language models, data pipeline construction, evaluation frameworks, and deployment strategies.
Education or degree requirements: Not explicitly mentioned in the JD.
Experienced with progressive scaling of ML models and automation of deployment/rollback pipelines.
Comfortable collaborating with cross-functional teams to integrate AI solutions into existing workflows.
Demonstrated ability to monitor production ML systems and implement continuous improvement methods such as canary deployments.