GenAI Frontline Engineer: Deploy & Orchestrate AI

Tiger Analytics Inc.; Bellevue (WA); On-site; USD 150,000 - 210,000; Full time

Job summary

Tiger Analytics seeks an experienced Forward Deployed Engineer to join our leading analytics consulting firm. You will partner with clients and engineering teams to turn complex business problems into production-grade GenAI and Agentic AI solutions, owning the journey from first conversation to live deployment.

Work spans designing, building, deploying, and scaling LLM applications, RAG systems, AI agents, and multi-agent workflows, with a focus on cloud production environments and

Qualifications

• 7+ years in software eng, ML eng, or AI eng.
• Strong Python programming skills.
• Solid grasp of APIs, system design, databases, cloud-native apps, and production deployments.
• JS/TS experience is a plus.

Responsibilities

• Partner directly with clients to understand complex business problems and translate them into GenAI and Agentic AI solutions.
• Design, build, deploy, and scale LLM applications, RAG systems, AI agents, and multi-agent workflows.
• Integrate AI solutions with enterprise data, APIs, databases, and business applications.
• Deploy, operate, and scale solutions in production cloud environments.
• Troubleshoot and optimize production systems for performance, scalability, reliability, security, and cost.
• Collaborate closely with architects, ML engineers, developers, and client stakeholders in fast-moving environments.

Skills

7+ years experience

Python

APIs

System design

Cloud deployments

LLMs

LangChain

Tools

Bedrock

Azure OpenAI

Vertex AI

OpenSearch

Semantic Kernel

Job description

Tiger Analytics seeks an experienced Forward Deployed Engineer to join our leading analytics consulting firm. You will partner with clients and engineering teams to turn complex business problems into production-grade GenAI and Agentic AI solutions, owning the journey from first conversation to live deployment.

Work spans designing, building, deploying, and scaling LLM applications, RAG systems, AI agents, and multi-agent workflows, with a focus on cloud production environments and