Job Opportunities API

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. Balance speed, quality, safety, cost, scalability, and maintainability. 3. Hands-On Build & Integration Build prototypes, production applications, APIs, integrations, internal tools, and workflow automation using models. Work closely with customer engineering teams to connect AI systems into existing enterprise platforms, data sources, identity systems, and business processes. Write reliable, maintainable code while moving quickly through evolving requirements. 4. Production Deployment & Adoption Own the path from prototype to production, including testing, rollout planning, observability, reliability, and operational readiness. Ensure deployed systems are secure, usable, measurable, and aligned with customer success criteria. Drive adoption by working with users, operators, engineering teams, and leadership. 5. Evaluation, Safety & Reliability Define evaluation methods to measure model quality, grounding, accuracy, latency, cost, safety, and workflow impact. Build feedback loops that detect failures, improve outputs, reduce hallucinations, and maintain trust in production usage. Ensure deployments follow security, privacy, access control, compliance, and responsible AI expectations. 6. Product & Research