𝗠𝗟 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 | 𝟴+ 𝘆𝗲𝗮𝗿𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 (𝗡𝗟𝗣, 𝗟𝗟𝗠𝘀, 𝗥𝗔𝗚, 𝗠𝗟𝗢𝗽𝘀). I specialize in bridging research and real-world deployment, turning ML prototypes into reliable, measurable systems that teams can ship, monitor, and scale on cloud infrastructure. Right now, I’m focused on advancing agentic AI systems, retrieval-augmented generation (RAG), and efficient LLM workflows, with an emphasis on quality, evaluation, and production readiness. 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 Over the last 8+ years, I’ve worked across software engineering + machine learning, building systems end-to-end—from data pipelines and model training to deployment, monitoring, and iteration. Alongside industry work, I’ve collaborated with faculty at SUNY Poly on research aimed at making LLMs more efficient and dependable, combining engineering execution with statistical rigor. 𝗞𝗲𝘆 𝗜𝗺𝗽𝗮𝗰𝘁 • Built and improved ML pipelines that achieved +30% accuracy improvement and -40% latency reduction • Led modernization/migration efforts delivering 99.9% uptime and ~35% cost optimization • Developed applied NLP/LLM projects including DeBERTa-based semantic matching, RAG assistants, and evaluation-driven experimentation • Mentored and supported large student cohorts as a Graduate Assistant, helping teams execute strong technical deliverables under real deadlines • Recognized for high-impact contributions with awards including Best Software Developer of the Year 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝗻𝗱 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 Outside of engineering, I’ve consistently taken ownership in leadership roles serving as Chairperson of the IEEE Student Branch and contributing as Program Head for NYBPC 2025 at SUNY, coordinating teams, stakeholders, and execution at scale. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗙𝗼𝗰𝘂𝘀 Core strengths: Production ML Systems, NLP/LLMs, RAG Pipelines, MLOps, Cloud Deployment, Experimentation & Evaluation Tools: Python, PyTorch, TensorFlow, Hugging Face, Docker, Kubernetes, AWS/GCP, REST/gRPC, CI/CD If you’re building AI systems that need to move beyond demos into production-scale impact, I’d love to connect, whether it’s for collaboration, research, or full-time ML engineering opportunities. Portfolio: https://jinankthakker.com GitHub: https://github.com/jinank Medium: https://medium.com/@jinankthakker Substack : https://mlinterviewprep.substack.com/