Software Engineer/Platform Engineer - Kubernetes + Golang/Python + AI

Job Summary

Meet the Team

You will be pivotal in contributing to the team responsible for designing and developing the next generation of scalable Kubernetes infrastructure with Foundational Model and GPU Platforms. This is a position for experienced engineers where you will lead the technical direction, ensuring the performance, reliability, and scalability of AI systems while collaborating closely with data scientists, researchers, and other engineering teams.

You will architect scalable infrastructure, and implement standard methodologies for Infrastructure as a Code with Golang along with developing platforms which offers LLMs and GPUs as service. Your leadership will influence the adoption of modern technologies and processes while mentoring junior engineers to strengthen the team s technical foundation. This role is perfect for someone with a deep passion for solving engineering challenges in the fast-paced AI/ML space, with a focus on delivering high-impact solutions at scale.

Core Responsibilities

As a Platform Engineer with AI/ML Experience you will:

• Design and develop scalable Kubernetes-based platform components, focusing on reliability, multi-tenancy, and efficient workload orchestration.
• Build and enhance platform capabilities to support Kubernetes workloads and emerging GenAI workloads (including GPU-based deployments), ensuring performance and cost efficiency.
• Develop backend services and platform tooling using Golang and/or Python, following strong software engineering practices.
• Contribute to AIOps initiative across platforms by collaborating with multi-functional teams, including SREs, Software Engineers to operationalize and optimize AI workflows effectively.
• Implement and improve observability frameworks (metrics, logs, traces) to enhance platform visibility and debugging.
• Build and maintain automation frameworks and Infrastructure as Code (IaC) to improve platform consistency and operational efficiency.
• Ensure platform reliability, scalability, and performance through meticulous engineering practices with operational rigor.
• Participate in on-call rotations, contributing to round the clock support, incident response, troubleshooting, and continuous reliability improvements.
• Conduct code reviews, establish standard processes, and mentor junior engineers.
• Stay updated on the latest trends in AI/ML to influence platform enhancements.

Minimum Qualifications / Requirements

• Experience: 5-7 years of software engineering experience
• Expertise in Golang or Python, with hands-on experience with Kubernetes platform Along with ML frameworks (TensorFlow, PyTorch).
• Assist in the design, implementation, and day-to-day operation of a Kubernetes platform supporting microservices architecture.
• Support and help improve the existing platform that hosts a large number of business applications.
• Help automate legacy platforms to enable infrastructure as code, increasing efficiency and consistency in platform management.
• Consistent track record in designing and deploying scalable machine learning systems in production.
• Experience building CI/CD pipelines for ML workflows, including model monitoring and retraining.
• Proficiency in cloud platforms and orchestration tools for distributed systems.
• Strong problem-solving and debugging skills for complex, large-scale systems.
• Experience in mentoring engineers and driving technical decision-making.

Preferred Qualifications / Requirements

• Sophisticated understanding in Kubernetes for managing production-grade systems and ensuring scalability.
• Sophisticated experience with Docker and orchestration of complex services.
• Expertise in Golang or Python
• Develop & enforce secure software development lifecycle
• Experience with architecting and optimizing workflows using Kubeflow pipelines, KServe, Airflow, and MLflow.
• Ability to design and implement efficient CI/CD workflows for LLM/GPU provisioning.
• Understanding of LangChain and experience designing RAG systems.
• Knowledge of integrating and scaling vector databases for real-world applications.
• Consistent record of designing and leading the development of distributed systems.
• Experience with implementing robust inter-service communication patterns and solving scalability issues.

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