Role Overview

As an AI/ML Platform Engineer, you will design, build, and scale cloud-native infrastructure that powers cutting-edge NLP and AI products. You’ll work across platform engineering, data orchestration, and model integration — enabling seamless deployment of large language models and related NLP capabilities within a robust AWS environment. This role blends deep software engineering expertise with hands-on machine learning operations, helping to shape the foundation of our next-generation AI platform.

Key Responsibilities

• Design, build, and maintain scalable AWS-based platforms to support AI/ML and NLP workloads.
• Translate product requirements into actionable technical roadmaps and engineering deliverables.
• Integrate and deploy large language models (LLMs) and other NLP solutions into distributed cloud systems.
• Develop and manage data pipelines and model orchestration frameworks for large-scale data processing.
• Implement and improve CI/CD, observability, and automation for ML pipelines and cloud deployments.
• Collaborate with product and research teams to operationalize NLP models and extract structured insights from unstructured data.
• Ensure scalability, reliability, and security across all components of the AI/ML platform.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 4+ years of experience building cloud-native, data-driven systems, ideally in production environments.
• Proven ability to architect and operate AWS infrastructure (e.g., ECS, Fargate, Lambda, S3, SageMaker).
• Strong foundation in Python software engineering, including testing, packaging, and database integration.
• Proficient in Linux-based development, Docker, Git, and modern CI/CD workflows.
• Experience working in Agile development environments and collaborating with cross-functional teams.

Preferred Skills

• Practical experience with large language models and NLP techniques (entity extraction, text classification, knowledge graphs).
• Familiarity with data science libraries (NumPy, pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow).
• Experience using orchestration and ML platforms such as Airflow, Prefect, Kubeflow, or SageMaker Pipelines.
• Background in data engineering — preparing and transforming structured and unstructured data for ML model use.