AI Forward Deployed Engineer

Engagement summary

The engineer will design and implement generative AI applications, AI agents, RAG pipelines, and enterprise workflows in close partnership with customer teams.

The role combines applied AI development with production software engineering, focusing on useful model behavior, reliable integrations, and measurable business outcomes across enterprise environments.

Key responsibilities

• Translate business processes into AI use cases, establish functional and quality baselines, and define testable acceptance criteria with customer stakeholders.
• Develop data ingestion, parsing, chunking, embedding, indexing, retrieval, and reranking pipelines. Preserve document permissions and manage data updates and traceability.
• Build LLM applications and agents that use enterprise APIs and tools. Implement state handling, retries, timeouts, structured outputs, and recovery from partial failures.
• Select and integrate suitable models and orchestration frameworks. Use NVIDIA NIM, NeMo, or other approved components where they meet deployment and application requirements.
• Create evaluation datasets and regression tests for factual grounding, task completion, retrieval quality, and tool correctness. Investigate poor responses using traces and reproducible examples.
• Implement safeguards for prompt injection, unauthorized data access, unsafe tool execution, and sensitive information exposure. Define approval steps for consequential automated actions.
• Containerize and deploy applications through CI/CD, configure monitoring, and optimize latency and operating cost while preserving quality and maintainability.
• Work with platform, SRE, and data teams to resolve production issues. Document architectures, integration contracts, test results, and operating procedures for customer handover.

Required experience and skills

• 7+ years in software engineering, applied machine learning, enterprise integration, or related delivery roles, including hands-on generative AI application development.
• Strong Python, REST API, testing, source control, and debugging skills, with experience delivering maintainable production software.
• Practical knowledge of LLM behavior, prompt design, embeddings, vector databases, retrieval pipelines, and evaluation methods.
• Experience building agent workflows with tool calling, explicit state, access boundaries, and error recovery using an appropriate orchestration framework.
• Working experience with PyTorch or TensorFlow and the ability to integrate model services into containerized applications.
• Ability to diagnose failures across data pipelines, retrieval, model endpoints, application logic, and infrastructure dependencies, and communicate findings clearly to customers.

Preferred experience

• Experience with LangChain, LangGraph, LlamaIndex, or comparable agent and retrieval frameworks; familiarity with multi-agent coordination.
• Exposure to NVIDIA NIM, NeMo, Triton, TensorRT-LLM, and CUDA fundamentals, particularly for applications deployed on enterprise GPU platforms.
• Experience with model adaptation, multimodal applications, responsible AI controls, and observability for production AI workflows.

Recommended certifications

• Preferred: NVIDIA-Certified Professional Generative AI LLMs (NCP-GENL) and Agentic AI (NCP-AAI).
• Foundational: NVIDIA-Certified Associate Generative AI LLM (NCA-GENL). Additional platform foundation: NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO).
• Certifications are preferred credentials. Candidates should demonstrate the corresponding skills through practical delivery and troubleshooting experience.

Expected deliverables and success measures

Deliver working AI applications, reusable integration components, evaluated retrieval and agent workflows, and production runbooks.

Success is demonstrated by agreed task completion and quality results, controlled data access, stable deployment, and customer acceptance.