Rajkumar C

About Rajkumar C

Machine Learning Engineer with 4+ years of experience building and deploying production-grade ML and AI systems in enterprise environments. Experienced across the full ML lifecycle—from data pipelines and model training to deployment, monitoring, and scaling using modern MLOps practices.Strong background in NLP, Generative AI, and LLMs, including fine-tuning and deploying models for real-world use cases. Comfortable working with distributed data systems, cloud platforms, Docker, Kubernetes, and CI/CD pipelines.I enjoy translating complex ML solutions into reliable, business-ready products and collaborating with cross-functional teams to deliver measurable impact.

Experience

1. Machine Learning Engineer

   Fidelity Investments

   Dec 2024 — Present · TX, US

   Led the design, development, and deployment of production-grade ML and Generative AI systems supporting enterprise-scale NLP and analytics use cases. Owned end-to-end delivery of LLM-powered solutions, from model selection and fine-tuning to scalable deployment and monitoring in production environments.Architected and maintained automated ML and LLM pipelines spanning data ingestion, feature engineering, training, evaluation, and deployment using Spark, SQL, Docker, Kubernetes, and CI/CD workflows. Fine-tuned and deployed LLMs (BART, T5, GPT) for large-scale document summarization and NLP workloads, supporting long-form documents and high-throughput enterprise traffic.Designed low-latency LLM inference services using Hugging Face Transformers, serving thousands of daily requests with optimized performance and reliability. Improved NLP model quality by 30%(ROUGE-L) through transfer learning, hyperparameter tuning, and system-level optimization.Hardened ML and LLM models into containerized microservices deployed on Kubernetes, exposing secure REST APIs and integrating GenAI capabilities into full-stack applications (Flask + React). Established CI/CD standards using Jenkins and GitHub Actions to enable automated deployments, versioning, rollback, and monitoring across ML systems.Acted as a technical partner to product, design, and business teams to productionize AI workflows, improving content-processing efficiency by 25% and enabling data-driven decision-making at scale

Education

• University of North Texas

  Master's degree, Computer Science