Senior Software Engineer, AI/ML Platform

Job Description

Senior Software Engineer, AI/ML Platform | [Company Name]

The Tone:
This is a full-time, hybrid or remote-friendly role at [Company Name]. The company is a leading technology organization focused on solving complex problems and creating a better future through intelligent solutions, particularly in Artificial Intelligence and Machine Learning. This position is crucial for building and maintaining the foundational AI/ML platforms that power the company’s groundbreaking products and services, enabling data scientists and ML engineers to operate efficiently. Your contributions will directly impact the development and deployment of next-generation intelligent solutions.

The TL;DR
• Role: Full Time
• Type: Hybrid/Remote-friendly
• Location: Hybrid/Remote

• Mission: Design, develop, and maintain scalable, high-performance infrastructure and tools that enable data scientists and ML engineers to build, train, and deploy machine learning models efficiently.
• Tech Stack: Python, Java, Go, Scala, C++, AWS, GCP, Azure, TensorFlow, PyTorch, Scikit-learn, Spark, Apache Kafka, Apache Flink, SQL/NoSQL databases, Docker, Kubernetes, serverless technologies, Feast, Tecton, AWS SageMaker, GCP Vertex AI, Azure ML, MLflow, Kubeflow, Snowflake, BigQuery, Redshift

What You’ll Actually Do
• Platform Design & Development: Architect and implement robust ML platform components, including MLOps pipelines, feature stores, model registries, and serving infrastructure.
• Tooling & Automation: Develop internal tools, libraries, and automation scripts to streamline the end-to-end ML lifecycle and improve team productivity.
• Performance & Scalability: Optimize platform components for performance, reliability, and cost-efficiency on cloud infrastructure to handle large datasets and high inference traffic.
• Collaboration & Support: Partner with data scientists, ML engineers, and product teams to understand their needs and integrate new capabilities into the platform.
• Best Practices & Mentorship: Advocate for and implement software engineering best practices, including CI/CD for ML systems, and provide technical leadership and mentorship to junior engineers.

The Must-Haves
• Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience, with a core focus on building AI/ML infrastructure or related data platforms.
• Experience: 5+ years of professional software development experience, with at least 2-3 years specifically focused on building and scaling AI/ML infrastructure or related data platforms.
• Skills: Strong proficiency in Python and at least one other compiled language (e.g., Java, Go, Scala, C++); extensive experience with major cloud providers (AWS, GCP, Azure) and their services; hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and distributed computing (Spark); solid understanding of data processing technologies; and practical application of MLOps principles and distributed system design.
• Bonus: Experience with Docker, Kubernetes, serverless technologies, feature stores (e.g., Feast, Tecton), managed ML services (e.g., AWS SageMaker, GCP Vertex AI, Azure ML), open-source MLOps platforms (e.g., MLflow, Kubeflow), big data technologies (e.g., Snowflake, BigQuery, Redshift), and excellent communication skills.