Forward Deployment Engineer - Frontier AI Deployments

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.

Key Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• Capture learnings from real customer deployments and share actionable feedback with Product, Research, Engineering, Safety, and GTM teams.
• Identify repeatable deployment patterns, product gaps, and opportunities to improve models and platforms.
• Help turn successful customer solutions into reusable technical patterns and deployment playbooks.

Required Qualifications

• Strong experience in software engineering, applied AI engineering, product engineering, solutions engineering, platform engineering, or technical consulting
• Strong hands-on programming experience with Python and at least one additional language such as TypeScript, JavaScript, Go, Java, C++, or Rust
• Experience building production software systems, APIs, integrations, backend services, data pipelines, or customer-facing applications
• Strong understanding of LLM application patterns such as prompts, context windows, RAG, embeddings, tool/function calling, agents, evaluations, and model orchestration
• Able to work directly with customer engineering and business teams in ambiguous, fast-moving environments
• Strong system design skills with practical judgment around reliability, security, scalability, latency, cost, and maintainability
• Excellent communication skills with the ability to explain complex technical ideas clearly to technical and non-technical stakeholders
• Ownership mindset with the ability to move from problem discovery to shipped production outcomes

Preferred Qualifications

• Experience deploying LLM, GenAI, agentic, or AI assistant systems in production
• Experience with OpenAI API, ChatGPT Enterprise, Codex, or similar AI platforms
• Experience with retrieval systems, vector databases, workflow automation, enterprise integrations, observability, and evaluation frameworks
• Experience working in customer-facing engineering roles such as Forward Deployment Engineer, Solutions Engineer, AI Deployment Engineer, Technical Lead, or Founding Engineer
• Experience deploying AI solutions in complex enterprise environments such as financial services, healthcare, government, legal, customer operations, software engineering, or enterprise productivity
• Experience turning repeated deployment learnings into reusable platform patterns, product feedback, or internal engineering playbooks

Technical Skill Areas

• AI Applications: LLMs, RAG, agents, tool calling, prompt design, context engineering, evaluations
• Software Engineering: Python, TypeScript, APIs, backend services, integrations, workflow automation
• Deployment: production rollout, observability, reliability, testing, monitoring, incident readiness
• Data & Systems: databases, vector search, enterprise APIs, authentication, permissions, data pipelines
• Cloud & Platform: Docker, Kubernetes, CI/CD, cloud platforms, serverless, infrastructure basics
• Security & Governance: access control, privacy, compliance, auditability, safe model deployment