Forward Deployment Engineer – Frontier AI Deployments

Accellor

Forward Deployment Engineer at Accellor deploying frontier AI models in customer production environments. Collaborating with teams to design practical AI solutions and ensure successful deployments.

Posted 6/22/2026full-timeSan Francisco • California • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

CloudDockerGoJavaJavaScriptKubernetesPythonRustTypeScript

About the role

Key responsibilities & impact

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.
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.
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.
Define evaluation methods to measure model quality, grounding, accuracy, latency, cost, safety, and workflow impact.
Capture learnings from real customer deployments and share actionable feedback with Product, Research, Engineering, Safety, and GTM teams.

Requirements

What you’ll need

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.
Ability 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.
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.
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

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account Accellor Website LinkedIn All Job Openings 201 - 500 employees 🏢 Enterprise ☁️ SaaS AI
Enterprise
SaaS Accellor is a company offering AI-driven solutions across various industries, focusing on enhancing efficiency and engagement through advanced applications and data strategies. Their services include leveraging artificial intelligence for enterprise applications, product engineering, and cloud services to transform industries such as healthcare, manufacturing, financial services, real estate, retail, travel, and hospitality. Accellor partners with technology leaders like Salesforce and Microsoft Dynamics 365 to deliver personalized, intelligent business applications. Committed to responsible AI practices, Accellor helps organizations harness the potential of data and AI to drive strategic decisions, automate operations, and provide superior experiences. Forward Deployment Engineer – Frontier AI Deployments Job not on LinkedIn 🔥 7 minutes ago 🏢🏡 San Francisco – Hybrid ⏰ Full Time 🟡 Mid-level 🟠 Senior ⛑ DevOps & Site Reliability Engineer (SRE) 🦅 H1B Visa Sponsor Cloud Docker Java JavaScript Kubernetes Python Rust TypeScript Go Apply Now Find Hiring Managers Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
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.
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.
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.
Define evaluation methods to measure model quality, grounding, accuracy, latency, cost, safety, and workflow impact.
Capture learnings from real customer deployments and share actionable feedback with Product, Research, Engineering, Safety, and GTM teams. 🎯 Requirements
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.
Ability 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.
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.
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 Apply Now 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score Similar Jobs Senior Site Reliability Engineer, Platform Infrastructure – Foundations 🕒 4 days ago Anyscale 51 - 200 🤖 Artificial Intelligence ☁️ SaaS 🏢 Enterprise Website LinkedIn All Job Openings Senior Site Reliability Engineer developing scalable infrastructure for AI applications on Anyscale’s platform. Collaborating with experts on Kubernetes, cloud-native technologies, and machine learning. 🏢🏡 San Francisco – Hybrid ⏰ Full Time 🟠 Senior ⛑ DevOps & Site Reliability Engineer (SRE) 🦅 H1B Visa Sponsor AWS Azure Cloud Distributed Systems Google Cloud Platform Grafana Kubernetes Linux Prometheus Python Ray Go Senior Site Reliability Engineer – FedRAMP 🕒 4 days ago Cisco 10,000+ employees 🔧 Hardware 🔐 Security 🏢 Enterprise Website LinkedIn All Job Openings Site Reliability Engineer managing FedRAMP compliant services for Cisco ThousandEyes. Collaborating with teams to ensure reliability, performance, and security of infrastructure. 🏢🏡 San Francisco – Hybrid 💵 $165k - $241.4k / year ⏰ Full Time 🟠 Senior ⛑ DevOps & Site Reliability Engineer (SRE) 🦅 H1B Visa Sponsor AWS Cloud Kubernetes Linux Puppet Python Terraform Unix Go Advanced Packaging Reliability Engineer 🕒 5 days ago OpenAI 201 - 500 🤖 Artificial Intelligence ☁️ SaaS 🏢 Enterprise Website LinkedIn All Job Openings Package Reliability Engineer leading reliability assessments and optimizations for HPC packages at OpenAI. Collaborating with cross-functional teams to enhance package performance and robustness. 🏢🏡 San Francisco – Hybrid 💵 $266k - $445k / year ⏰ Full Time 🟠 Senior 🔴 Lead ⛑ DevOps & Site Reliability Engineer (SRE) 🦅 H1B Visa Sponsor Assembly Electron Ray Senior SRE Engineer 🕒 6 days ago Plaud 201 - 500 🤖 Artificial Intelligence 🔧 Hardware ☁️ SaaS Website LinkedIn All Job Openings Senior SRE Engineer ensuring reliability and performance of Plaud.ai’s AI products at scale. Design and operate highly available, scalable cloud-native systems for AI workloads. 🏢🏡 San Francisco – Hybrid ⏰ Full Time 🟠 Senior ⛑ DevOps & Site Reliability Engineer (SRE) AWS Azure Cloud Distributed Systems Google Cloud Platform Java Kubernetes Python Go Senior Site Reliability Engineer 🕒 June 10 Airbyte 51 - 200 ☁️ SaaS 🤝 B2B 🤖 Artificial Intelligence Website LinkedIn All Job Openings Infrastructure and reliability engineer on the Data Replication team at Airbyte. Handling Kubernetes clusters, CI/CD pipelines, and enhancing observability and tooling for AI integration. 🏢🏡 San Francisco – Hybrid 💵 $196k - $255k / year 💰 $150M Series B - Airbyte on 2021-12 ⏰ Full Time 🟠 Senior ⛑ DevOps & Site Reliability Engineer (SRE) 🦅 H1B Visa Sponsor AWS Cloud Google Cloud Platform Grafana Kubernetes Prometheus Terraform View More DevOps Jobs 🌐 Worldwide Built by Lior Neu-ner. I'd love to hear your feedback — Get in touch via DM or support@remoterocketship.com Search Search Jobs by country Search jobs by city Search jobs by job title Search entry-level jobs Search junior-level jobs Search senior-level jobs Search jobs by tech stack Search jobs by contract type Search remote internships Search remote part-time jobs Remote jobs Anywhere in the World Companies Hiring Anywhere in the World Companies Hiring Sales People Anywhere in the World Companies Hiring Software Engineers Anywhere in the World Resources Advice Tips for finding remote jobs Interview questions and answers Resume examples Cover letter examples Post a job Affiliates Privacy policy Terms of service Job board SEO course AI Apply Copilot OpenClaw job finder Jobs by Country Remote jobs anywhere in the world (Worldwide remote jobs) Remote jobs United States Remote jobs Australia Remote jobs Brazil Remote jobs Canada Remote jobs France Remote jobs Ireland Remote jobs Germany Remote jobs Netherlands Remote jobs Spain Remote jobs UK Popular Jobs Remote data analyst jobs Remote customer support jobs Remote executive assistant jobs Remote marketing jobs Remote product designer jobs Remote product manager jobs Remote project manager jobs Remote recruiter jobs Remote sales jobs Remote software engineer jobs Jobs by Type Remote full-time jobs Remote part-time jobs Remote contract jobs Remote internship jobs Remote entry-level jobs Remote jobs with no experience required Remote junior jobs (1-3 years of experience) Digital nomad jobs Remote jobs with no degree required Freelance remote jobs Temporary remote jobs Remote jobs hiring now Stay at home mom jobs

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Hard Skills & Tools

PythonTypeScriptJavaScriptGoJavaC++RustAPIsLLM application patternsworkflow automation

Soft Skills

communication skillsownership mindsetsystem design skillsability to work in ambiguous environmentspractical judgmentcollaborationproblem discoveryfeedback sharingtechnical explanationstakeholder engagement