Senior Machine Learning Engineer at CLBPTS - All Lines of Business — Bengaluru, IND | Base Career | Base Career

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Senior Machine Learning Engineer

CLBPTS - All Lines of Business

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Qualifications

Ph.D., Master’s degree, or equivalent practical experience in Computer Science, Artificial Intelligence, Machine Learning, Operations Research, Statistics, or a related technical field.
5+ years of relevant experience with a Master’s degree, or 3+ years with a Ph.D., applying machine learning to real-world problems.
Strong Python programming skills and experience building production-quality ML, GenAI, or data systems.
Hands-on experience with PyTorch and modern deep-learning stacks; experience with Hugging Face, LLMs, VLMs, diffusion models, or multimodal models is strongly preferred.
Experience with data-centric AI or GenAI methods, such as synthetic-data generation, data-quality measurement, dataset curation, weak supervision, model-based labeling, active learning, deduplication, or data augmentation.
Experience designing experiments and interpreting results through statistical analysis, ablation studies, benchmark evaluation, and error analysis.
Strong understanding of model training, inference, evaluation, and production monitoring.
Ability to evaluate research papers, identify practical value, and implement useful techniques in real-world systems.
Experience building scalable data or ML pipelines using distributed compute, cloud storage, batch processing, or workflow orchestration.
Strong written and verbal communication skills, including experience preparing technical proposals, design documents, experiment reports, and stakeholder presentations.

Description

Design and build data-centric Generative AI methods for synthetic data generation, multimodal data curation, augmentation, filtering, deduplication, and data-quality assessment.
Develop and evaluate synthetic-data pipelines for text, speech, vision, and multimodal GenAI use cases, including controllable generation, provenance tracking, safety checks, and domain adaptation.
Build evaluation frameworks that connect data quality with downstream model performance through benchmark design, ablation studies, error analysis, and model-feedback loops.
Research, prototype, and implement modern generative AI techniques, including LLM/VLM-based data generation, fine-tuning, instruction tuning, preference optimization, and model-based data labeling.
Build scalable data and ML pipelines for data acquisition, cleaning, transformation, metadata extraction, embedding generation, labeling, training, and evaluation.
Develop production-quality code for batch and real-time ML workflows, including model inference, feature processing, data validation, monitoring, and operational automation.
Translate research papers and emerging GenAI techniques into practical systems that improve data quality, model performance, and customer-facing AI outcomes.
Partner with modeling, product, infrastructure, and domain teams to define data requirements, quality standards, evaluation criteria, and delivery plans.
Operate across the full development lifecycle, including research, prototyping, experimentation, productionization, testing, CI/CD, monitoring, runbooks, and production support.

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Description

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