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We are seeking a highly skilled and experienced Software Engineer to design, develop, and optimize systems and applications in our Machine Learning (ML) platform. We want the highest levels of technical talent and programming skills, as well as a keen desire to deeply understand our products and services to push our technology forward. You’ll work alongside the most creative engineering talent in the industry around the world.
As an AI Platform Software Engineer, you will work closely with engineering teams, data scientists, and business partners to develop systems that support high-performance model training, deployment, and monitoring. What you will accomplish:
Design and implement robust, scalable, and secure systems to support a wide range of machine learning use cases, including large-scale training, real-time inference, and agentic AI applications.
Evaluate and adopt emerging technologies and frameworks to enhance platform capabilities, optimize GPU usage, and improve performance and cost efficiency for high-throughput model serving.
Collaborate with cross-functional teams, including data engineers, software engineers, data scientists, product managers, and researchers, to align platform development with business goals and integrate new LLM and agentic features.
Define and implement end-to-end automated ML workflows covering data preprocessing, model training, hyperparameter tuning, deployment, and monitoring.
Architect and build APIs, services, and orchestration layers tailored for LLMs and agentic AI, using frameworks like LangChain, Langfuse, Spring AI, and LlamaIndex to accelerate feature development.
Ensure adherence to enterprise-level governance, security, and compliance standards while maintaining scalability, reliability, and maintainability across both production and experimental environments.
What you will bring:
BS, MS, or PhD in Computer Science, Engineering, or a related field, with 4+ years of software development and architecture experience, including at least 2 years focused on machine learning platforms.
Proven expertise in designing and building ML platforms, including training pipelines, inference services, and large-scale, secure APIs to support LLM-based and agentic applications.
Strong programming skills in Python, Java, or Scala, with hands-on experience using ML frameworks such as TensorFlow or PyTorch.
Familiarity with containerization and orchestration tools like Docker and Kubernetes, as well as big data technologies including Spark, Hadoop, and Kafka.
Experience with MLOps practices, including CI/CD pipelines and tools such as MLflow or Kubeflow; familiarity with is a plus.
Demonstrated success implementing AI/LLM solutions at scale and collaborating with product and research teams to deliver experimental and production-ready AI-driven features using frameworks like LangChain, LlamaIndex, and Spring AI.
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