Solution Deployment Engineer Jobs (NOW HIRING)

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

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Product & Research Feedback

• 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.

• 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

• The ideal candidate is a hands-on engineer who can embed with customers, understand their hardest problems, build AI-powered systems quickly, and take ownership until those systems are running in production.
• They should be comfortable writing code, designing systems, working with executives, partnering with engineers, handling ambiguity, and making practical trade-offs under real delivery pressure.
• This role requires a builder’s mindset, strong customer empathy, product judgment, technical depth, and the ability to convert frontier AI capability into measurable production impact.