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# Forward Deployed Engineer

- **Company:** Tiger Analytics
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, BigQuery, Business Software, Cloud Computing, Databases, Python (Programming Language), Machine Learning, OpenAI, Azure Active Directory, Software Deployment, Software Engineering, TypeScript, Enterprise Search, Enterprise Data Management, Spring Cloud, LangChain, Retrieval-Augmented Generation, Open-source Models, Large Language Models, Multi-Agent Systems, Llamaindex, Agentic-AI, Vertex AI Vector Search, Amazon Bedrock, Azure OpenAI API, Machine Learning Operations, Claude, Evaluation of Large Language Models, Invoking Functions, Google Gemini, Semantic Kernel, Serverless Computing, OpenSearch, Agent2Agent Protocol
- **Published:** October 1, 2026
- **Apply:** https://www.thejobnetwork.com/job/693b7395-0da9-41d7-82e6-0e1a3ecc74ac/forward-deployed-engineer-fde-genai-agentic-ai

## About the Role

Engineering Foundation

 * 7+ years in software engineering, application development, ML engineering, or AI engineering.
 * Strong Python programming skills.
 * Solid grasp of APIs, system design, databases, cloud-native applications, and production deployments.
 * JavaScript/TypeScript experience is a plus.

GenAI / Agentic AI Expertise

 * Proven experience building and deploying end-to-end GenAI and/or Agentic AI solutions.
 * Hands-on experience with LLMs - OpenAI, Anthropic Claude, Google Gemini, and open-source models.
 * Hands-on experience with RAG, vector databases, embeddings, tool/function calling, and AI agents.
 * Experience with agent orchestration, multi-agent systems, or agentic workflows.
 * Familiarity with frameworks such as LangGraph, LangChain, LlamaIndex, Google ADK, or similar.
 * Working knowledge of MCP, A2A, or similar agent integration protocols.

Cloud & Production

 * Hands-on experience with at least one major hyperscaler - AWS, Azure, or GCP - including targeted expertise in:
 * AWS Stack: Designing workflows via Amazon Bedrock (Claude, Llama), deploying scalable RAG using OpenSearch Serverless (Vector Engine), and configuring Bedrock Guardrails for client VPCs.
 * Azure Stack: Deploying enterprise LLMs via Azure OpenAI Service (PTU optimization), building hybrid RAG systems with Azure AI Search, and orchestrating flows using Azure AI Foundry and Semantic Kernel.
 * GCP Stack: Scaling applications within Vertex AI, managing production pipelines, structuring enterprise RAG with Vertex AI Vector Search + BigQuery, and building agentic flows with Gemini APIs.
 * Exposure to LLM evaluation, observability, monitoring, guardrails, or LLMOps.

The FDE Mindset

 * Strong customer-facing communication and sharp problem-solving instincts.
 * Comfortable operating independently, navigating ambiguity, and moving fast from problem * solution * production.
 * Equally at home with business and technical stakeholders.

## Description

We're looking for a Forward Deployed Engineer who thrives at the intersection of client problem-solving and hands-on AI engineering. You'll partner directly with clients and engineering teams to turn complex business challenges into production-grade GenAI and Agentic AI solutions - owning the journey from first conversation to live deployment.

Requirements

 * 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, ambiguous environments.