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Tejal Mane
Mid-level Machine Learning Engineer specializing in GenAI, LLMs, and real-time ML systems
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About
Built and deployed a production long-form article summarization system using BART/T5/PEGASUS, tackling real-world constraints like token limits, latency/quality tradeoffs, and factual drift via chunking/merge logic and constrained decoding. Uses pragmatic Python-based pipeline orchestration (scheduled jobs, modular scripts, logging/retries) and iterates with stakeholder feedback to make outputs genuinely useful for content workflows.
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Key Strengths
- Built and deployed an end-to-end production text summarization system using transformer models
- Practical solutions for long-input constraints (chunking + coherent merge strategy)
- Balances quality vs latency via decoding optimization and model tradeoff evaluation
- Improves factual consistency through constrained generation and targeted edge-case review
- Designs reliable ML workflows with clear success metrics, baselines, and rollback discipline
- Pipeline-oriented orchestration with task boundaries, logging, and failure handling
- Effective collaboration with non-technical stakeholders to refine outputs for real usability
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