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.

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