Senior Software Engineer, AI Platform

Job Description

Senior Software Engineer, AI Platform | [Company Name]

The Tone:
This is a full-time role at [Company Name], offering a hybrid work model with options for remote flexibility. [Company Name] operates as a leading technology firm, deeply committed to pushing the boundaries of artificial intelligence. This position is central to building and scaling the foundational infrastructure that powers the company’s cutting-edge AI products and services, making it a critical role for shaping the future of intelligent solutions across various industries.

The TL;DR
• Role: Full Time
• Type: Hybrid work model with remote flexibility
• Location: Hybrid with remote flexibility

• Team: AI Platform team
• Mission: Design, build, and scale the foundational infrastructure that powers cutting-edge AI products and services.
• Tech Stack: Python, Go, Java, C++, AWS, GCP, Azure, Docker, Kubernetes, SQL, NoSQL, MLflow, Kubeflow, Sagemaker, TFX, TensorFlow, PyTorch, Kafka, Flink

What You’ll Actually Do
• Platform Architecture: Architect and develop robust, scalable, and high-performance microservices and APIs for the core AI platform.
• ML Infrastructure Development: Design and implement data pipelines and machine learning infrastructure that supports model training, deployment, and monitoring at scale.
• Cross-Functional Collaboration: Collaborate closely with AI/ML researchers, data scientists, and product managers to translate research prototypes into production-ready solutions.
• System Optimization & Standards: Optimize existing systems for performance, reliability, and cost-efficiency, identifying and addressing bottlenecks, while driving best practices in software development including CI/CD.
• Technical Mentorship: Mentor junior engineers, share knowledge, and contribute to a culture of technical excellence and continuous learning.

The Must-Haves
• Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• Experience: 5+ years of professional experience in software development, with a significant focus on backend systems or platform engineering. This includes designing and building robust systems from the ground up.
• Skills: Strong proficiency in at least one modern programming language, with Python being highly preferred for AI/ML contexts. Extensive hands-on experience with major cloud platforms such as AWS, GCP, or Azure, and practical expertise in containerization technologies like Docker and Kubernetes. A solid understanding of distributed systems, microservices architectures, and API design principles is essential, alongside familiarity with various database technologies (SQL and NoSQL) and data warehousing solutions.
• Bonus: Experience with MLOps tools and frameworks like MLflow, Kubeflow, Sagemaker, or TFX. A foundational understanding of machine learning concepts, algorithms, and common frameworks such as TensorFlow or PyTorch. Prior experience building and deploying real-time inference systems, contributions to open-source projects, or experience with stream processing technologies like Kafka or Flink are also highly valued.