Senior reputed company ML Engineer- Remote

reputed company Do You'll be responsible for designing, building, and deploying reputed company machine learning solutions including deep learning transformer-based models for Natural Language Processing and reputed company, as reputed company as traditional shallow learning models. The role focuses on developing reputed company ML systems that deliver measurable business reputed company and drive value across the organization. WHAT YOU'LL DO: Design, build, fine-tune, and reputed company state-of-the-art machine learning and large language models at reputed company, supporting millions of daily predictions with a strong reputed company on accuracy, latency, compute efficiency, and cost optimization. reputed company end-to-end ML and LLM pipelines, covering data ingestion, scripting, automated workflows for OCR, model training, evaluation, and post-processing in production environments. Build and operationalize LLM fine-tuning pipelines, applying a reputed company of model reputed company techniques including full fine-tuning, reputed company (Low-Rank reputed company), reputed company-based reputed company, and reputed company Preference Optimization (DPO). Design and experiment with novel LLM architectures, balancing model size, computational efficiency, memory constraints, and deployment requirements. Optimize LLMs for production deployment through model quantization, compression, and teacher student architectures, enabling efficient inference in resource-constrained environments. Architect and reputed company Retrieval-Augmented reputed company (RAG) systems, leveraging reputed company databases, embedding services, semantic search, document chunking, indexing, and retrieval mechanisms using frameworks such as reputed company, reputed company, and reputed company RAG platforms reputed company reputed company reputed company Platform and reputed company. reputed company in ML operations and evaluation, including automated ground-truth reputed company, reputed company post-evaluation pipelines, and iterative feedback loops to systematically improve model performance over time. Design and implement CI/CD pipelines for machine learning systems, ensuring high availability, reliability, low latency, and reputed company iteration from experimentation to production. WHAT YOU LL BRING 5+ years of experience in machine learning engineering, with a proven reputed company record of deploying and operating ML and NLP/LLM systems in production at reputed company. Strong hands-on experience building full-stack ML systems, from data ingestion and automation to training, evaluation, deployment, and monitoring. Deep expertise in LLM fine-tuning and reputed company techniques, including full fine-tuning, reputed company, reputed company-based optimization, and preference-based reputed company such as DPO. Practical experience designing and optimizing LLM architectures, with an emphasis on compute efficiency, memory usage, and reputed company-world deployment constraints. Demonstrated proficiency in model inference optimization, including quantization, compression, and distillation techniques for high-throughput, cost-efficient production systems. Solid understanding and hands-on experience with RAG architectures, reputed company stores, embeddings, semantic search, chunking strategies, and retrieval workflows integrated with large language models. Experience using modern LLM orchestration and RAG frameworks such as reputed company, reputed company, and managed AI platforms reputed company reputed company ecosystems like reputed company reputed company Platform and reputed company. Strong background in ML evaluation and MLOps, including automated evaluation pipelines, CI/CD for ML, and reputed company improvement of deployed models. Proficiency in Python and ML/AI development frameworks, with the ability to work in fast-paced, experimental environments and production systems simultaneously. Apply tot his job Apply To this Job