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Niharika Govinda
Junior Machine Learning Engineer specializing in NLP, data pipelines, and LLM workflows
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Built and shipped a production LLM-powered decision system that replaced a slow, inconsistent manual review process by turning messy text into structured, auditable outputs behind an API. Demonstrates strong end-to-end ownership of reliability and operations (schema validation, retries/fallbacks, latency/cost controls, monitoring for drift) and a disciplined approach to evaluation and regression testing. Experienced collaborating with non-technical reviewers to define success criteria and deliver interpretable outputs that get adopted.
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Key Strengths
- Built and deployed an LLM decision system converting unstructured text into structured, auditable outputs
- Production reliability engineering for LLMs (schema enforcement, validation, retries, safe fallbacks)
- Pragmatic evaluation approach without perfect ground truth (small labeled set, human agreement tracking)
- Strong production operations mindset (monitoring reliability, cost, and drift; rapid rollback via versioning)
- Workflow/pipeline reliability design (idempotency, timeouts/backoff, checkpointing)
- Performance optimization of pipelines and LLM workflows (caching, batching, safe parallelism, reducing calls)
- Model/retrieval/prompt selection driven by measurable trade-offs (accuracy/latency/cost/explainability)
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