This role focuses on building scalable backend services, automating cloud infrastructure, and enabling reliable deployment of AI-powered solutions. The ideal candidate will work closely with software engineers, data scientists, and AI/ML specialists to transform innovative AI capabilities into secure, scalable, production-ready applications.
Candidates with experience in cloud-native development, infrastructure automation, LLM-based applications, and modern DevOps practices will be well suited for this opportunity.
Develop and maintain scalable backend applications, APIs, and distributed services using Python.
Design and automate cloud infrastructure using Infrastructure as Code (IaC) tools such as Pulumi, Terraform, or similar technologies.
Build and maintain CI/CD pipelines to streamline application development, testing, deployment, and release management.
Support the deployment and operationalization of AI/ML models, LLM applications, Retrieval-Augmented Generation (RAG) solutions, and AI agent workflows.
Work with AI/ML teams to integrate model-serving capabilities, inference services, and data processing pipelines into production environments.
Implement containerized application deployments using Docker and Kubernetes or other cloud-native technologies.
Improve system reliability and performance through monitoring, logging, tracing, alerting, and proactive troubleshooting.
Establish reusable engineering components, automation frameworks, and development standards that improve team productivity.
Apply best practices for application security, testing, code quality, scalability, latency, and cloud cost optimization.
Investigate production issues, conduct root cause analysis, and implement long-term corrective solutions.
Collaborate with architecture, security, data, product, and platform engineering teams to deliver reliable technical solutions.
Participate in code reviews, technical discussions, and knowledge-sharing activities to promote engineering excellence.
5+ years of professional software engineering or related development experience.
Strong hands-on Python programming experience, including the development of production-grade backend services and APIs.
Experience designing and supporting cloud-based applications and distributed systems.
Practical experience with Infrastructure as Code tools such as Pulumi, Terraform, or equivalent solutions.
Working knowledge of at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (Google Cloud Platform).
Experience with CI/CD pipelines, infrastructure automation, and modern software deployment practices.
Understanding of production support, system monitoring, troubleshooting, and application performance optimization.
Familiarity with containerization technologies such as Docker and container orchestration platforms such as Kubernetes.
Strong analytical, problem-solving, communication, and collaboration skills.
Ability to work independently, take ownership of technical deliverables, and adapt to evolving project requirements.
Preferred Qualifications
Exposure to MLOps, AI/ML infrastructure, model deployment, or inference-serving technologies.
Experience supporting Large Language Model (LLM) applications, RAG pipelines, or agentic AI solutions.
Familiarity with Databricks, Azure AI Foundry, or comparable AI/ML platforms.
Knowledge of serverless architectures, cloud security, observability frameworks, and distributed application design.
Experience developing reusable platform capabilities, developer tools, and automated engineering workflows.
Bachelor's degree in Computer Science, Software Engineering, or a related discipline is preferred.
