Senior Machine Learning Engineer in India at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Machine Learning Engineer in India.

In this role, you will work on building and optimizing advanced machine learning systems that power secure, scalable, and efficient AI-driven applications used by thousands of global customers. You will be responsible for developing and fine-tuning small language models, improving inference efficiency, and deploying models across edge, mobile, and server environments under strict performance constraints. The position requires a strong balance of research-driven ML engineering and production-focused implementation, with an emphasis on latency, scalability, and reliability. You will contribute to end-to-end MLOps pipelines, ensuring smooth transitions from data ingestion to production deployment. The environment is highly technical and collaborative, with a strong focus on applied AI innovation and real-world impact. You will also play a key role in monitoring, evaluating, and continuously improving model performance in production systems.

• Fine-tune and train small language models using frameworks such as Hugging Face, TRL, and parameter-efficient methods like LoRA, QLoRA, and PEFT to improve performance and efficiency.
• Optimize machine learning models through quantization, pruning, and knowledge distillation techniques to ensure lightweight and high-performance inference.
• Design and deploy models across edge devices, mobile environments, and local servers while meeting strict latency and hardware constraints.
• Build and maintain end-to-end MLOps pipelines covering data ingestion, model training, validation, deployment, and monitoring in production environments.
• Monitor and analyze model performance metrics including accuracy, latency, and compute utilization (CPU/GPU) to ensure operational stability.
• Develop and use benchmarking frameworks and evaluation systems to continuously assess and improve model quality.

• Strong experience in machine learning engineering, with hands-on expertise in training and fine-tuning small or large language models.
• Proficiency with Hugging Face ecosystem and adapter-based fine-tuning methods such as LoRA, QLoRA, or similar techniques.
• Solid understanding of model optimization techniques including quantization, pruning, and knowledge distillation for efficient deployment.
• Experience building production-grade MLOps pipelines, including CI/CD workflows, experiment tracking, and model versioning.
• Proven ability to deploy ML models to edge, mobile, or constrained environments with performance and latency considerations.
• Strong programming skills in Python and familiarity with ML frameworks such as PyTorch or TensorFlow.
• Experience with model monitoring, performance tuning, and system-level debugging in production environments.
• Bonus: Knowledge of ONNX export and cross-platform inference optimization.

• Competitive salary aligned with experience and market standards.
• Comprehensive medical insurance coverage for employees and their families.
• Fully covered life insurance premiums for financial security.
• Flexible working hours with paid time off to support work-life balance.
• Gym reimbursement and wellness support programs.
• Childcare reimbursement and family-friendly benefits.
• Access to a global, innovation-driven environment focused on applied AI and scalable ML systems.