As a Principal AI Engineer, you will serve as the technical leader for AI agent development across the organization. Your work will center on designing robust, scalable systems that empower teams to build intelligent, automated solutions grounded in enterprise requirements.
Key Responsibilities
- Architect and implement orchestration frameworks that support advanced agent behaviors including tool integration, memory management, retrieval-augmented generation (RAG), and multi-step workflows.
- Define engineering standards for agent lifecycle management, state handling, and context modeling to ensure consistency and reliability.
- Lead the integration of Gemini models through Vertex AI, focusing on secure, efficient API usage and intelligent model routing.
- Develop internal SDKs and abstractions that standardize how engineering teams interact with AI platforms, enabling faster and safer adoption.
- Optimize prompt structures, token usage, grounding methods, and structured output patterns to improve accuracy and reduce cost.
- Build reusable agent frameworks in Java, Python, Go, or TypeScript, supporting both low-code and high-code development paths.
- Design secure integration patterns for agents accessing enterprise systems such as Salesforce, Snowflake, SharePoint, and ServiceNow.
- Establish telemetry, logging, and evaluation pipelines to monitor agent performance, trace execution flows, and ensure operational reliability.
- Implement safeguards against hallucinations, prompt injection, and policy violations, ensuring trustworthy AI behavior.
- Collaborate with security teams to enforce data protection, role-based access controls, and compliance standards.
- Guide engineering teams adopting AI-assisted development tools, CLI integrations, and internal AI platforms.
- Produce technical documentation, reusable libraries, and reference implementations for diverse developer audiences.
- Review and advise on AI-enabled application architectures across the company.
- Improve inference efficiency through caching, streaming, batching, and parallelization strategies.
- Conduct performance evaluations and benchmarking across agent and model configurations.
Qualifications
You bring deep experience in software engineering with a focus on distributed, cloud-native systems. A strong foundation in backend development and AI infrastructure is essential.
- 10+ years of software engineering experience, with proven expertise in distributed systems and backend architecture.
- Hands-on coding in Python and at least one of Go, Java, or TypeScript.
- Production experience with large language models, including prompt engineering, tool calling, RAG, embeddings, and agent frameworks.
- Familiarity with Vertex AI, Gemini APIs, OpenAI, or similar enterprise AI platforms.
- Solid grasp of API design, microservices, Kubernetes, and cloud-native environments.
- Experience with orchestration tools such as LangChain, LlamaIndex, or custom frameworks.
- Knowledge of vector databases, embedding pipelines, and retrieval methods.
- Understanding of authentication, authorization, and enterprise security practices.
- Track record of building reusable platforms rather than one-off solutions.
Preferred Background
- Experience developing multi-agent systems or autonomous workflow engines.
- Work with model evaluation pipelines and AI quality metrics.
- Familiarity with structured output enforcement using JSON schemas or function calling.
- Integration experience with enterprise data platforms like Snowflake, Salesforce, ServiceNow, or SharePoint.
- Knowledge of inference cost modeling and optimization techniques.
- Contributions to internal developer platforms or SDK ecosystems.
- Background in AI safety, red-teaming, or model robustness testing.
Technology Environment
You'll work with Gemini, Vertex AI, LLMs, LangChain, LlamaIndex, Python, Go, Java, TypeScript, Kubernetes, microservices, APIs, vector databases, RAG, prompt engineering, JSON schemas, function calling, Salesforce, Snowflake, SharePoint, ServiceNow, internal APIs, MCP (Model Context Protocol), and RBAC.
Compensation & Benefits
This role offers a salary range of $186,400 – $279,600, along with restricted stock units, incentive compensation, and a performance bonus. Additional benefits include health coverage, paid time off, professional development opportunities, and relocation support for onsite placement. The position is onsite and supports qualified candidates with reasonable accommodations.
