AI Engineer/Forward-Deployed Engineer

WTW•Austin, TX

16d•$125,000 - $250,000

About The Position

The AI Engineer / Forward Deployed Engineer is responsible for designing, building, integrating, and operating production-grade AI solutions that solve real business problems inside complex enterprise environments. The role combines hands-on software engineering, AI solution architecture, customer or business stakeholder engagement, and end-to-end delivery ownership. Unlike a traditional AI or software engineering role focused only on internal product backlogs, this role works close to the operational problem. The engineer translates ambiguous business needs into deployed AI-enabled workflows, connects enterprise systems and data sources, validates output quality, and ensures solutions are reliable, secure, cost-effective, and adopted by users. This position is ideal for an experienced Solutions Architect, Staff Engineer, or Technical Lead with a strong enterprise engineering background and a passion for applying it to AI-enabled systems. You’ll bring deep expertise across modern full-stack technologies (.NET, Azure, SQL, React/Angular), along with experience in distributed systems, observability, and AI tooling such as LLMs, retrieval pipelines, and agentic workflows. Acting as a bridge between business and technology, you’ll work across product, data science, architecture, and engineering teams—mentoring others, resolving production challenges, and scaling prototypes into robust, enterprise-grade solutions that deliver real impact.

Requirements

Strong enterprise engineering background
Deep expertise across modern full-stack technologies (.NET, Azure, SQL, React/Angular)
Experience in distributed systems
Experience in observability
Experience with AI tooling such as LLMs, retrieval pipelines, and agentic workflows

Responsibilities

AI solution delivery: Design and build AI-enabled applications, copilots, agents, extraction pipelines, prediction interfaces, and decision-support tools using foundation models, retrieval-augmented generation, structured outputs, and orchestration frameworks.
Forward deployed problem solving: Work directly with business teams, product owners, clients, or operational users to understand real workflows, constraints, data quality issues, and adoption barriers, then translate these into working technical solutions.
LLM and agent engineering: Build and tune LLM workflows, prompt strategies, schema-driven extraction, tool-calling patterns, agent orchestration, evaluation loops, and human-in-the-loop controls.
Enterprise integration: Integrate AI solutions with enterprise systems, APIs, data platforms, document repositories, workflow tools, observability platforms, and identity and access management services.
Production engineering: Ensure AI solutions meet enterprise standards for reliability, scalability, latency, maintainability, cost control, logging, monitoring, and operational support.
Evaluation and quality assurance: Create evaluation datasets, test harnesses, validation tools, regression checks, and quality review workflows to measure accuracy, extraction quality, hallucination risk, and business usefulness.
Architecture and technical leadership: Define solution architecture, engineering standards, reusable patterns, and implementation approaches for AI-enabled platforms and services.
Data and knowledge readiness: Work with engineering, data, and business teams to prepare structured and unstructured data, improve metadata, design retrieval strategies, and identify gaps in source content.
Security, privacy, and governance: Embed access controls, audit logging, data protection, responsible AI controls, security review, and compliance requirements into the AI delivery lifecycle.
Adoption and enablement: Support users through demos, pilots, training, feedback loops, documentation, and iterative improvement so that deployed AI solutions create measurable business value.