Forward Deployment Engineer

Accellor is an AI-native services firm purpose-built for the post-ChatGPT era. Free from legacy constraints, we focus on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. Our mission is to operationalize AI at scale and unlock sustained enterprise value. 

Our offerings span AI solutions, data services, enterprise applications, and product engineering, tailored to industry-specific needs across healthcare, life sciences, telecom, retail, financial services, and technology. By leveraging design thinking and technology-agnostic architectures, we ensure faster time-to-value and seamless interoperability. 

With a proven track record of enabling Fortune 100 enterprises and global innovators, Accellor stands as a trusted partner for organizations seeking to harness the full potential of AI. Our vision is clear: to build intelligent, connected ecosystems that deliver measurable outcomes and redefine the future of enterprise transformation. 

Forward Deployment Engineer — Frontier AI Deployments

Function: Forward Deployment Engineering / Applied AI Engineering / Model Deployment
Role Type: Forward Deployment Engineer / Customer-Embedded AI Engineer

Role Summary:

Accellor is looking for a Forward Deployment Engineer to work directly with strategic customers and help deploy frontier AI models into real production environments.

This role combines hands-on software engineering, AI application development, solution design, customer collaboration, and production deployment. The engineer will understand customer problems, design practical AI solutions, build working systems, integrate with existing platforms, and drive adoption in production.

The ideal candidate is a strong builder who can operate in ambiguous environments, move quickly, write high-quality code, and turn frontier AI capabilities into measurable business impact.

Key Responsibilities:

1. Customer Discovery & Technical Scoping

Work directly with customer engineering, product, business, and domain teams to understand workflows, technical constraints, and high-value AI opportunities.

Translate ambiguous customer problems into clear technical plans, success criteria, and delivery milestones.

Identify where models can deliver measurable value in real production workflows.

2. Solution Design & Architecture

Design AI-powered systems that integrate models with customer data, tools, APIs, applications, and security controls.

Define practical architecture for model usage, retrieval, context management, tool calling, orchestration, evaluation, monitoring, and production reliability.