Software Specialist Engineer – Applied AI & Cloud Native

Company Overview Our client is a leading technology-driven organization focused on building innovative, cloud-native software products and intelligent AI-powered solutions. The organization emphasizes modern engineering practices, scalable architectures, DevSecOps, SRE, and the practical adoption of Generative AI and agentic technologies.

Position Overview

As a Software Specialist Engineer – Applied AI & Cloud Native, you will design, develop, deploy, and optimize scalable software and AI-powered applications. You will work across cloud infrastructure, microservices, application development, GenAI, RAG pipelines, and AI agent orchestration. The role requires strong software engineering fundamentals combined with hands-on experience in cloud platforms, Infrastructure as Code, containerization, LLM integration, and production-grade AI applications.

Responsibilities

• Design and develop scalable cloud-native applications using microservices, PaaS/FaaS, and serverless architectures.
• Build and deploy AI/ML and Generative AI applications using LLMs, RAG pipelines, vector databases, and AI agents.
• Integrate LLM platforms such as OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or open-source models.
• Develop AI agent and multi-agent workflows using frameworks such as LangChain, LangGraph, LangFuse, LangSmith, or equivalent technologies.
• Build and manage containerized applications using Docker and Kubernetes.
• Implement Infrastructure as Code using Terraform.
• Develop applications using Python, Go, Java, C#/.NET, Bash, or similar programming languages.
• Implement cloud solutions across Azure, AWS, or GCP, including their AI/ML services.
• Design and implement RAG, prompt engineering, vector search, LLM evaluation, and AI application observability capabilities.
• Apply DevSecOps and SRE practices throughout the software development lifecycle.
• Implement automated unit testing, code quality, security, and continuous integration practices.
• Use tools such as GitHub, Azure DevOps, SonarQube, MLflow, and modern monitoring/observability platforms.
• Optimize application and cloud infrastructure costs and demonstrate FinOps awareness.
• Create and interpret architecture and engineering artifacts including BCDs, sequence diagrams, activity diagrams, state diagrams, ER/data models, and data-flow diagrams.
• Apply OOP/OOD, data structures, algorithms, code instrumentation, and AI-augmented spec-driven development.
• Collaborate with engineering, product, architecture, and AI/ML teams to deliver high-quality products rapidly.

Skills & Experience

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Machine Learning, or a related discipline.
• 6–9 years of software engineering experience, with strong hands-on development experience.
• Strong programming experience in one or more of Python, Go, Java, C#/.NET, or Bash.
• Hands-on experience with Kubernetes, Docker, and Terraform.
• 3+ years of experience building AI/ML, Generative AI, or agentic AI applications.
• Strong hands-on experience with:
• LLM integration
• RAG pipelines
• Prompt engineering
• Vector databases
• LLM evaluations
• AI agent orchestration
• Experience with OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, or open-source LLMs.
• Experience with LangChain, LangGraph, LangFuse, LangSmith, or equivalent AI/agentic frameworks.
• Experience with Azure, AWS, or GCP and cloud-native application development.
• Knowledge of PaaS, FaaS/serverless, microservices, and application-level Infrastructure as Code.
• Experience with PyTorch, TensorFlow, or equivalent ML frameworks is desirable.
• Strong understanding of OOP/OOD, data structures, algorithms, software architecture, and system design.
• Experience with DevSecOps, SRE, CI/CD, GitHub, Azure DevOps, and SonarQube.
• Experience with automated/unit testing frameworks.
• Exposure to MLflow and monitoring/observability tools is an advantage.
• Understanding of FinOps and cloud cost optimization.
• Strong understanding of software architecture diagrams and technical documentation.
• Ability to work in an AI-augmented, spec-driven software development environment.