Senior AI Engineer, NLP & Training Data - 11316

Team: Engineering - India

Location: Bangalore, India

Commitment: Mid Level

Workplace Type: remote

The Impact of a Senior AI Engineer, NLP & Training Data at Coupa: 

Coupa already operates production ML models and frontier model integrations across its AI platform. The Sr. Engineer, AI / Machine Learning will own the model training and iteration workstream, building the fine-tuning pipelines, evaluation harnesses, and iterative training loops that take our model capabilities to the next level. Working closely with the Principal Architect, you will turn architecture decisions into production training infrastructure.

What You’ll Do

• Build and own the end-to-end model fine-tuning pipeline: data preprocessing, training, evaluation, and model registry.
• Implement and optimize fine-tuning techniques (QLoRA, LoRA, PEFT, full fine-tune) for our training workloads.
• Design and maintain evaluation harnesses with task-specific benchmarks and automated regression testing.
• Drive the training iteration loop: analyze results, diagnose failure modes, improve data and configuration.
• Implement experiment tracking, hyperparameter optimization, and reproducible training workflows.
• Collaborate on training data strategy with data engineering, including synthetic data generation.
• Evaluate model quality across safety, accuracy, latency, and cost dimensions.
• Contribute to model serving architecture and inference optimization.
• Mentor ML engineers across the team.

What You Will Bring to Coupa

• 5+ years of software engineering experience, with 2+ years focused on ML/NLP systems.
• Hands-on experience fine-tuning large language models with parameter-efficient methods.
• Strong knowledge of transformer architectures, tokenization, and training optimization.
• Experience building production ML training pipelines with experiment tracking.
• Proficiency in Python, PyTorch, and distributed training frameworks.
• Experience with GPU-based training infrastructure in the cloud.
• Strong evaluation methodology: designing benchmarks, measuring quality, detecting regressions.
• Experience with RLHF, DPO, or other alignment techniques is a strong plus.
• BS/MS in Computer Science, Machine Learning, or equivalent experience.