Software Engineer, AI Agents and Software Platforms

NVIDIA NVIDIA · Semiconductors · Tel Aviv, Israel +1

Software Engineer role focused on architecting and building scalable frameworks and APIs for integrating AI agents with NVIDIA's internal and external platforms, including designing CI/CD pipelines, optimizing performance, and developing internal tooling for agent onboarding and observability.

What you'd actually do

  1. Architect and build robust, scalable frameworks and APIs to integrate AI agents with NVIDIA's suite of developer tools, simulation platforms, and enterprise software.
  2. Collaborate with research, applied AI, and product teams to understand agent capabilities and translate them into production-ready services and features.
  3. Design and implement CI/CD pipelines and MLOps for the AI agent and SW simulation products, including testing, deployment, monitoring, and continuous improvement.
  4. Optimize the performance, latency, and resource consumption of deployed AI agents, ensuring they operate efficiently within our compute infrastructure.
  5. Develop internal tooling and automation to streamline the onboarding of new AI agents and enhance the observability of agent fleets.

Skills

Required

  • BSc or above in Computer Science, Computer Engineering, or a related field, or equivalent experience.
  • 3+ years of hands-on experience in software engineering, with a focus on backend systems, cloud services, or infrastructure.
  • Proven experience with AI/ML frameworks (e.g., PyTorch, TensorFlow) and a strong understanding of large language models (LLMs), transformers, and agent-based architectures (e.g., LangChain, LlamaIndex).
  • Proven experience in architecting, building and deploying complex integrations between AI agents and external tools, APIs, or software services.
  • Expert-level programming skills in Python.
  • Experience designing, building, and maintaining RESTful APIs, gRPC, and other service-to-service communication protocols.
  • Excellent problem-solving skills and the ability to navigate complex, ambiguous technical challenges.
  • Strong communication and interpersonal skills, with a proven ability to collaborate effectively across multidisciplinary teams.

Nice to have

  • C++ is a strong plus.
  • Hands-on experience building or fine-tuning LLMs or other generative models.
  • Prior experience with MLOPs, and agentic infrastructure.
  • Contributions to open-source AI/ML projects.
  • Experience with Infrastructure as Code (Terraform, Ansible).
  • Prior experience in developing platforms for internal developer communities.
  • Knowledge of cloud platforms (AWS, GCP, Azure), container orchestration (Kubernetes, Docker), and building scalable microservices.
  • Familiarity with vector databases (e.g., Milvus, Pinecone) and model serving infrastructure (e.g., Triton Inference Server).

What the JD emphasized

  • architecting, building and deploying complex integrations between AI agents and external tools, APIs, or software services
  • agent-based architectures
  • AI agent interaction
  • AI agent fleets
  • AI agents

Other signals

  • architecting AI agent interaction frameworks
  • integrating AI agents with developer tools and platforms
  • designing scalable SW development platforms for generative AI