Mid-level Machine Learning Engineer specializing in deep learning and generative AI
San Jose, CAMachine Learning Engineer5 years experienceMid-LevelFinancial ServicesInsuranceInformation Technology & Services
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About
AI/ML engineer who has deployed transformer-based NLP systems to production via Python REST APIs and Kubernetes on AWS/Azure, with a strong focus on latency optimization (p95), reliability, and scalable orchestration. Demonstrates pragmatic model tradeoff decision-making and strong stakeholder collaboration—improving adoption by making outputs more actionable with summaries, extracted fields, and confidence indicators.
Experience
Machine Learning EngineerMetLife
Machine Learning EngineerHexaware Technologies
Education
University of Alabama at Birminghammaster, Computer Science (2025)
Kakatiya Institute of Technology and Sciencesbachelor, Computer Science (2021)
Key Strengths
Built and deployed production NLP system for large-scale unstructured text analysis
Reduced inference latency via warm models, input limits, batching, and concurrency/resource tuning
Production reliability focus: containerized deployment, monitoring, versioning, rollback
Kubernetes-based deployment with scaling, health checks, and rolling updates (no downtime)
Metrics-driven performance optimization using p95 latency to identify and fix peak-load contention
Pragmatic model selection balancing accuracy, latency, and cost (chose smaller fine-tuned model for production constraints)
Effective collaboration with non-technical stakeholders; translated business goals into AI requirements