Software Engineer AI/ML Platform & Cloud Engineering

About the Role

Accede Solutions is seeking an experienced Software Engineer with strong Python, cloud engineering, and backend development expertise to support the development and deployment of next-generation AI/ML applications.

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.

Key Responsibilities

• 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.

Required Qualifications

• 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.