10850 – Sr. Software Engineer, Applied AI
$103,170–$158,873 year
On-site · Irvine, California, United States
Job Summary
Senior Sr. Software Engineer - Applied AI responsible for designing, building, and deploying real-world AI solutions including LLM-powered applications, RAG pipelines, agentic workflows, and full-stack AI systems. Develop production-ready AI software, integrate models into cloud environments, and collaborate across engineering, product, and data teams to ensure governance, privacy, and security standards. Responsibilities include prototyping PoCs/MVPs, refining prompts, improving model performance, and delivering scalable AI capabilities with frontend interfaces and backend services. Requires hands-on experience with Python, TensorFlow/PyTorch, Hugging Face, vector databases (Pinecone, ChromaDB, FAISS), MCP, cloud AI platforms (AWS SageMaker/Bedrock, Azure OpenAI), ML Ops, and full-stack/cloud-native development (React, Docker, Kubernetes). Team-oriented, growth-focused environment with a strong emphasis on collaboration, agility, and inclusivity.
Required Qualifications
- Bachelor’s or master’s degree in computer science, engineering or a related field
- 8+ years of software engineering experience, including 3+ years focused on AI/ML solution development
- Proven experience delivering production-ready AI systems, including LLM-based applications, traditional ML models, RAG pipelines, and agentic/automated workflows
- Hands-on expertise with modern agent frameworks such as AutoGen, LangGraph, LlamaIndex, CrewAI, and n8n
- Strong proficiency in Python and core AI/ML tooling, including TensorFlow or PyTorch, the Hugging Face ecosystem, and common AI orchestration frameworks
- Deep experience with vector databases (Pinecone, ChromaDB, FAISS) and embedding-based retrieval techniques
- Advanced understanding of prompt engineering, RAG architecture, model evaluation, and LLM optimization strategies
- Practical experience with cloud AI platforms such as AWS SageMaker/Bedrock, Azure OpenAI, or Azure AI Foundry, along with ML Ops best practices (CI/CD, testing, model monitoring, observability)
- Proficiency with full-stack and cloud-native development, including React, microservice APIs, relational/NoSQL databases, Docker, and Kubernetes
- Experience implementing the Model Context Protocol (MCP) to improve context sharing, interoperability, and coordination across multi-agent systems
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