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Jitesh Kumar S
Junior Machine Learning Engineer specializing in NLP, computer vision, and MLOps
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About
ML/LLM engineer with Meta experience building production AI systems for near real-time user-report classification and summarization under strict latency (<250ms), safety, cost, and privacy constraints. Has hands-on MLOps/orchestration experience (Airflow, Spark, MLflow, Kubernetes, Docker, GitHub Actions) plus observability (Prometheus/Grafana) and applies rigorous evaluation, staged rollouts, and A/B testing to keep agent workflows reliable in production.
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Key Strengths
- Built and deployed near real-time LLM system for user report classification/summarization with <250ms latency target
- End-to-end pipeline redesign using Airflow + Spark + MLflow to improve feature freshness and reduce latency (~600ms to ~250ms)
- Strong production reliability mindset: KPIs, guardrails, timeouts, fallbacks, multi-level testing, monitoring/alerts, rollback readiness
- Data-driven iteration on model/prompt choices balancing quality vs latency/cost; executed side-by-side comparisons and gradual rollouts with feature flags
- Cross-functional collaboration with non-technical stakeholders by translating goals into measurable metrics, prototypes, and dashboards
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