Sr Machine Learning Engineer - AI

Accountabilities: Fine-tune and train small language models using Hugging Face, TRL, and adapter-based techniques such as LoRA, QLoRA, and PEFT. Optimize models for efficient inference through quantization, pruning, knowledge distillation, and other model-compression approaches. Deploy machine learning models to edge devices, mobile platforms, and local servers while meeting demanding latency and resource constraints. Build end-to-end MLOps pipelines covering data ingestion, experimentation, model development, evaluation, deployment, and production operations. Establish and maintain model evaluation frameworks, benchmarking processes, and custom test suites to measure model quality and performance. Monitor production models for accuracy, inference latency, CPU/GPU utilization, and other relevant operational metrics. Contribute to continuous improvements in AI deployment workflows, model efficiency, reliability, and scalability. Requirements Hands-on experience developing, training, and fine-tuning small language models or other transformer-based models using Hugging Face and related tooling. Strong knowledge of adapter-based fine-tuning methods, including LoRA, QLoRA, and PEFT. Practical experience with model optimization techniques such as quantization, pruning, and knowledge distillation. Experience deploying machine learning models to edge devices, mobile environments, or local/on-premises infrastructure. Ability to design and implement end-to-end MLOps pipelines from data ingestion through production deployment. Experience monitoring machine learning systems in production, including model accuracy, latency, and hardware utilization. Strong understanding of model evaluation, benchmarking, and performance optimization. Experience with experiment tracking, model registries, and ML-focused CI/CD practices is valuable. Knowledge of ONNX export and cross-platform inference is an advantage. Strong problem-solving skills and the ability to work effectively in a collaborative engineering environment. Benefits Remote working opportunity in India. Opportunity to work on applied AI, SLMs, model optimization, and production machine learning systems. Exposure to edge, mobile, and resource-constrained AI deployment environments. Opportunity to work with modern machine learning and MLOps technologies. Inclusive and collaborative workplace culture that values diverse perspectives and backgrounds. Professional growth opportunities through work on advanced AI engineering challenges.