Forward Deployed AI Engineer

Forward Deployed Ai Engineer

This is a hybrid in-office position and we are only open to candidates in the Greater Austin, Texas Area or willing to relocate to Austin, Texas at this time.

Role Summary:

As a Forward Deployed AI Engineer, you will work closely with some of our most strategic enterprise customers to deploy LLM-powered agentic solutions tailored to their specific business challenges. You'll be part of a nimble, high-impact engineering team embedded with customers—building custom agentic workflows, optimizing infrastructure, and shaping product feedback directly into our platform. This is a hands-on engineering role for those who thrive at the edge of product innovation and customer engagement.

What You Will Do:

Build and deploy LLM-powered solutions (agents, RAG pipelines, tools integration) directly with enterprise customers.

Collaborate with cross-functional teams including Product Managers, Solutions Architects, and Customer Success to understand client requirements and deliver tailored AI solutions.

Iterate quickly on customer-facing use cases, incorporating feedback loops to improve model and system performance.

Support customers in implementation, fine-tuning, and integration of LLMs in production environments.

Share insights from deployments with core engineering and product teams to influence roadmap and feature prioritization.

Occasionally travel to customer sites or participate in virtual workshops and implementation sessions.

Key Responsibilities:

Customer-Embedded Engineering:

Work closely with enterprise customers to understand their workflows and define solution architectures involving AI agents, LLMs, and retrieval systems.

Translate real-world customer problems into deployable AI workflows using planning, memory, retrieval, and tool integration.

Hands-on AI Systems Development:

Implement and optimize agentic AI systems using LLMs and tool orchestration frameworks (e.g., LangChain, RAG pipelines).

Conduct prompt engineering, performance evaluation, and fine-tuning to improve solution effectiveness and reliability.

Collaborate with product and platform teams to identify reusable patterns and influence roadmap based on deployment learnings.

Customer Feedback & Support:

Serve as a technical point of contact during deployments and early adoption phases.

Capture feedback from real-world usage and help prioritize improvements and fixes.

Requirements:

Must-Have:

3+ years of experience in ML, NLP, or software engineering, with recent exposure to LLMs and agentic workflows.

Strong programming skills in Python and relevant libraries (Transformers, LangChain, FastAPI, etc.).

Experience with cloud infrastructure and deploying ML models in real-world settings.

Ability to work directly with customers and translate business problems into AI solutions.

Familiarity with vector databases, retrieval systems, and prompt engineering best practices.

Nice-to-Have:

Prior experience in a forward-deployed, customer-facing, or consulting engineering role.

Exposure to compliance and enterprise deployment constraints (e.g., PII, data governance).

Background in MLOps, data pipelines, or orchestration tools (Ray, Airflow, etc.).