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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer - **Company:** Penguin Recruitment Ltd - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $175,000.0 - $215,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Microsoft Azure, Business Systems, Data Centers, Data Cleansing, Linux, Distributed Systems, Python (Programming Language), Key Management, Machine Learning, Network Segmentation, Open Source Technology, Open Web Application Security, Ansible, Prometheus, Software Deployment, Software Engineering, Systems Integration, Management of Software Versions, AI Infrastructure, Enterprise Data Management, Scripting, Google Cloud, Enterprise Software Applications, Pytorch, Retrieval-Augmented Generation, Large Language Models, Grafana, Prompt Engineering, Model Validation, Multi-Cloud, Llamaindex, Agentic-AI, Kubernetes, HuggingFace, Ray Serve, Triton Inference Server, Machine Learning Operations, Nim (Programming Language), Artificial Intelligence Governance, Api Design, Terraform, Docker - **Published:** October 17, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=b2d6ad12b2ad9fa3 ## About the Role This role is ideal for an experienced AI engineer who enjoys solving complex technical challenges, translating customer requirements into practical solutions, and delivering secure, production-ready AI systems., * 7+ years of experience in software engineering, ML engineering, AI infrastructure, or distributed systems. * Strong Python programming skills - testing, packaging, async, and API development, not just scripting. * GPU model serving at scale. NVIDIA AI Enterprise (NIM, NeMo, Triton Inference Server) is what we deploy most often, and prior experience with it is a significant plus - but equivalent experience with vLLM, TGI, Ray Serve, SageMaker, or similar inference stacks is fully acceptable, and we'll support your ramp-up on the NVIDIA stack. * Production experience with Linux, Docker, Kubernetes, APIs, and distributed systems. * Hands-on experience building LLM applications that carried real production traffic - retrieval-augmented generation, agentic workflows, tool integration, evaluation, and guardrails. * Experience deploying beyond a single managed cloud: multi-cloud, on-premises, or hybrid environments. * Strong communication and technical judgment, with experience explaining tradeoffs to non-specialist stakeholders., * Familiarity with AI-specific security risks (for example, the OWASP Top 10 for LLM Applications) and comfort partnering with security teams on enterprise controls. * Experience with PyTorch and Hugging Face. * Experience with agentic frameworks such as LangGraph or LlamaIndex. * Experience with MLOps platforms such as MLflow or Kubeflow. * Experience with model fine-tuning, data preparation, or other model-adaptation techniques. * Experience deploying in sovereign, regulated, disconnected, or air-gapped environments. * Familiarity with observability and infrastructure-automation tooling (OpenTelemetry, Prometheus/Grafana, Terraform, Ansible). * Knowledge of AI governance, model risk, data privacy, and responsible-AI practices. * Experience delivering in customer-facing or professional-services environments. ## Description As an Senior AI/ML Engineer, you will be a hands-on technical expert within our post-sales delivery organization, designing, building, deploying, and troubleshooting enterprise AI solutions for our customers. You will work across AI models, agentic applications, security, MLOps, and the NVIDIA AI Enterprise software stack, supporting public-cloud, sovereign, on-premises, and hybrid environments., Build Production AI Solutions * Architect and implement applications using commercial, open-source, and customer-developed models. * Build agentic solutions incorporating retrieval, tool use, workflow orchestration, memory, evaluation, guardrails, and observability. * Evaluate models and determine when to use prompt engineering, retrieval-augmented generation, fine-tuning, or other adaptation techniques. * Integrate AI applications with enterprise data, APIs, tools, and business systems. Design Enterprise AI Environments * Design and deploy solutions using the NVIDIA AI Enterprise software stack and the broader AI ecosystem. * Build portable solutions for AWS, Azure, GCP, on-premises data centers, and hybrid environments. * Work with containers, Kubernetes, GPU-enabled infrastructure, networking, storage, identity, and platform services. * Implement reliable LLMOps and MLOps practices for testing, deployment, monitoring, governance, versioning, and rollback. Embed Security * Address AI-specific risks such as prompt injection, insecure tool use, sensitive-data exposure, excessive permissions, and software supply-chain vulnerabilities. * Apply enterprise security practices including access controls, encryption, secrets management, network segmentation, and auditability. * Support deployments with data-privacy, regulatory, sovereignty, or disconnected-environment requirements. Deliver Customer Solutions * Translate customer requirements and constraints into practical architectures and implementations. * Contribute directly to technical discovery, proofs of concept, production deployments, and operational support. * Troubleshoot complex issues across AI applications, orchestration platforms, integrations, and infrastructure. * Clearly explain technical options, limitations, and tradeoffs to customers and internal stakeholders.