Senior Software Engineer, AI Platform

Job Description

Senior Software Engineer, AI Platform | [Your Company Name]

The Tone:
This is a full-time role at [Your Company Name], with remote options available. We are a technology leader creating intelligent, data-driven solutions for complex problems, focused on democratizing access to advanced AI, making it usable and impactful for businesses of all sizes. This role is crucial for designing, developing, and deploying the scalable AI infrastructure that powers our products and services. You will contribute to technologies at the intersection of complex engineering and AI research.

The TL;DR
• Role: Full Time
• Type: Full-time
• Location: Remote

• Team: AI Platform
• Mission: Build and maintain the core infrastructure that enables data scientists and machine learning engineers to rapidly develop, train, and deploy AI models at scale.
• Tech Stack: Python, Go, Java, AWS, GCP, Azure, Docker, Kubernetes, PostgreSQL, MySQL, Cassandra, MongoDB, Redis, TensorFlow, PyTorch, scikit-learn

What You’ll Actually Do
• System Architecture: Design and architect robust, scalable, and highly available systems for model training, inference, data pipelines, and experiment tracking.
• Platform Development: Develop and implement core services and APIs for our machine learning platform, primarily using modern programming languages.
• Performance Optimization: Optimize the performance of critical AI components, focusing on efficiency, latency, throughput, and resource utilization across cloud infrastructure.
• Stakeholder Collaboration: Collaborate with data scientists, ML engineers, and product teams to understand needs, translate requirements into technical solutions, and provide guidance.
• Technical Leadership: Mentor junior engineers, share knowledge, and actively contribute to the technical growth and capabilities of the team.

The Must-Haves
• Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• Experience: 5+ years of professional software development experience, with a significant portion focused on building scalable backend systems or AI/ML infrastructure.
• Skills: Strong proficiency in at least one modern programming language (e.g., Python, Go, Java), extensive experience with cloud platforms (AWS, GCP, Azure) and cloud-native architectures, proficiency with containerization (Docker) and orchestration (Kubernetes), solid understanding of distributed systems and microservices architecture, and excellent problem-solving skills with attention to detail.
• Bonus: Experience with MLOps tools (e.g., MLflow, Kubeflow, Sagemaker, Airflow), contributions to open-source projects, experience with real-time data streaming (e.g., Kafka, Kinesis), knowledge of data warehousing solutions and ETL processes, or experience with performance tuning of large-scale systems.