> Markdown version of [/jobs/ext/3379692-ai-ml-engineer](https://www.wearedevelopers.com/jobs/ext/3379692-ai-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** SAP SE - **Location:** Newport Beach, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Code Review, Continuous Delivery, Continuous Integration, Data Transformation, Database Analysis, Python (Programming Language), Performance Tuning, Regression Testing, SAP (Applications), Software Engineering, System Testing, Scripting, Pytorch, Large Language Models, Multi-Agent Systems, Information Technology, Machine Learning Operations, TensorRT, Api Design - **Published:** September 9, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-ai-ml-engineer-agentic-ai-newport-beach-ca--c202c949-5e0b-4862-88a9-7e616f5856bd ## About the Role Education: BS, MS, or PhD in Computer Science, ML, or a related field. Experience: 6+ years building and shipping ML systems, with 3+ years hands-on with LLMs and agents in production. Core technical skills * Expert Python; strong fundamentals: system design, testing, modularity, async, API design * PyTorch; working knowledge of fine-tuning and PEFT methods (LoRA, QLoRA) * LLM application development: prompting, structured outputs, tool calling, context management * Inference optimization: vLLM, TensorRT-LLM, quantization (int8, int4, GPTQ, AWQ) * Human-in-the-loop annotation workflows at scale Agent frameworks and orchestration (production experience with at least three) * LangGraph / LangChain * CrewAI, AutoGen/AG2, or equivalent * MCP (Model Context Protocol) * Coding agents: Claude Code, OpenCode, or similar Evaluation and observability (production experience with at least two) * Langfuse, LangSmith, Arize, or equivalent * LLM-as-judge evaluators, CI/CD eval gates Leadership * Demonstrated track record mentoring engineers and raising team technical quality * Drives decisions in ambiguous, fast-moving environments * Writes design docs that earn buy-in across teams Nice to have * Continued pretraining or fine-tuning pipelines (SFT, DPO, RLHF), Application Programming Interface (API), Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Business Model, Business Processes, Business Solutions, Cloud Computing, Code Reviews, Computer Science, Continuous Deployment/Delivery, Continuous Integration, Cost Control, Customer Acquisition, Customer/Client Research, Database Analysis, ERP (Enterprise Resource Planning), Establish Priorities, Finance, Instrumentation, Leadership, MCP - Microsoft Certified Professional, Memory Hardware, Mentoring, Precision Testing, Production Systems, Prototyping, Purchasing/Procurement, Python Programming/Scripting Language, Regression Testing, SAP, Software Design, Software Development, Supply Chain, System Test, Team Player, Technical Leadership, Test Design ## Description Were building specialized foundation models and AI agents that accelerate SAP customers data transformation journeys. The agents you build will directly power SAPs Autonomous Enterprise, where AI runs core business processes end-to-end across finance, supply chain, HR, and procurement at global scale. Youll set technical direction, define how we architect and scale multi-agent systems, and raise the engineering bar across a global team. Youll work directly with pretraining and fine-tuning team leads in Europe, India, and early-adopter customers. We want someone who has shipped agentic systems in production and knows where they break. What youll do * Architect and lead multi-agent systems: design, orchestration patterns, failure modes, memory, planning, and human-in-the-loop * Own the path from prototype to production: containerization, guardrails, cost and latency optimization, scalable serving * Define the teams evaluation strategy: offline/online harnesses, trajectory quality, tool-call accuracy, regression testing, CI/CD eval gates * Lead instrumentation and observability: tracing, span capture, automated scoring, closing the trace eval fix loop * Drive tool integration architecture via MCP across multiple product teams * Mentor junior and mid-level engineers through code and architecture reviews; set engineering standards, For information on the responsible use of AI in our recruitment process, please refer to our Guidelines for Ethical Usage of AI in the Recruiting Process. Please note that any violation of these guidelines may result in disqualification from the hiring process.