Build scalable Python backend services, APIs, cloud-native applications, and AI-powered solutions. Develop LLM, RAG, agentic, and multi-agent systems; deploy workloads with Docker and Kubernetes; maintain CI/CD pipelines; and operate applications across major cloud platforms. Collaborate cross-functionally to deliver secure, tested, observable, and production-ready software while optimizing performance, scalability, latency, and cost.
We are seeking a highly motivated Software Engineer to join our growing AI/ML team. The ideal candidate combines strong software engineering fundamentals with hands-on experience developing AI-powered applications and services.
In this role, you will build scalable backend systems, APIs, and cloud-native solutions while contributing to Generative AI initiatives, including LLM-based applications, Retrieval-Augmented Generation (RAG) pipelines, and Agentic AI systems. You will leverage modern AI-assisted development tools to accelerate delivery while maintaining high standards for code quality, testing, security, and maintainability.
This position is ideal for engineers who enjoy solving complex technical challenges, rapidly prototyping innovative solutions, and delivering production-ready software in a collaborative, fast-paced environment.
Responsibilities- Design, develop, test, and deploy scalable backend services and APIs, primarily using Python.
- Build modular, maintainable, and well-tested software following engineering best practices and clean architecture principles.
- Utilize AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Windsurf, or similar solutions to improve development productivity while maintaining code quality and accountability.
- Rapidly prototype and iterate on new features while balancing speed, maintainability, and long-term scalability.
- Develop and integrate LLM-powered capabilities, including prompt engineering workflows and Retrieval-Augmented Generation (RAG) solutions.
- Design and implement agentic and multi-agent systems capable of task orchestration, reasoning, and tool utilization.
- Containerize applications using Docker and deploy scalable workloads through Kubernetes.
- Build and maintain CI/CD pipelines to automate testing, integration, deployment, and release management.
- Deploy and manage applications across cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
- Collaborate with data scientists, product managers, architects, and software engineers to translate business requirements into production-ready solutions.
- Optimize application performance, latency, scalability, and operational costs, including AI-driven services.
- Implement security, monitoring, logging, and observability best practices across software platforms.
- Stay current with emerging software engineering practices, AI technologies, cloud-native architectures, and developer productivity tools.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related discipline, or equivalent professional experience.
- 3-6 years of professional software engineering experience delivering production-grade applications.
- Strong proficiency in Python and software design principles.
- Experience developing REST APIs, GraphQL APIs, and microservices-based architectures.
- Experience working with relational and/or NoSQL databases.
- Strong knowledge of Git, automated testing, code review, debugging, and software development lifecycle best practices.
- Hands-on experience using AI-assisted software development tools such as GitHub Copilot, Cursor, Claude Code, Windsurf, Amazon Q Developer, or comparable platforms.
- Experience with Docker and Kubernetes.
- Experience implementing CI/CD pipelines using tools such as GitHub Actions, GitLab CI, Jenkins, ArgoCD, or similar.
- Experience deploying and operating solutions on AWS, Azure, or GCP.
- Working knowledge of Large Language Models (LLMs), prompt engineering concepts, and AI application development frameworks such as LangChain, LangGraph, Google ADK, or similar.
- Strong analytical, problem-solving, communication, and collaboration skills.
- Experience building production-grade RAG solutions.
- Experience developing agentic AI applications and multi-agent systems.
- Experience with vector databases such as Pinecone, Weaviate, FAISS, or Milvus.
- Familiarity with model serving technologies such as vLLM, Triton Inference Server, or TorchServe.
- Experience with MLOps platforms such as MLflow, Kubeflow, or Weights & Biases.
- Experience with Infrastructure as Code (IaC) tools such as Terraform or Helm.
- Knowledge of Responsible AI, AI governance, and AI safety practices.
- Experience supporting enterprise-scale software, AI, manufacturing, or automotive solutions.
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