{"id":59437,"url":"https://alion.io/job/accellor-forward-deployment-engineer-frontier-ai-deployments","title":"Forward Deployment Engineer - Frontier AI Deployments","company":{"id":456,"name":"Accellor","domain":"accellor.com","url":"https://alion.io/company/accellor","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":133000,"max_usd":292000,"period":"year","method":"role_country_seniority_unknown","sample_n":4335},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"ChatGPT","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Go","optional":false},{"name":"JavaScript","optional":false},{"name":"LLM","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rust","optional":false},{"name":"TypeScript","optional":false},{"name":"CI/CD","optional":true},{"name":"Context Engineering","optional":true},{"name":"Docker","optional":true},{"name":"Kubernetes","optional":true},{"name":"OpenAI","optional":true},{"name":"OpenAI Codex","optional":true}],"status":"closed","first_seen_at":"2026-08-13T00:00:00Z","employer_posted_date":"2026-08-13","last_verified_at":"2026-10-22T21:39:54Z","board_verified":false,"closed_at":"2026-10-22T21:39:54Z","days_open":70,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":70},"description":"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.\nOur 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.\nWith 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.\nForward Deployment Engineer - Frontier AI Deployments\nFunction: Forward Deployment Engineering / Applied AI Engineering / Model Deployment\nRole Type: Forward Deployment Engineer / Customer-Embedded AI Engineer\nRole Summary:\nAccellor is looking for a Forward Deployment Engineer to work directly with strategic customers and help deploy frontier AI models into real production environments.\nThis 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.\nThe 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.\nKey Responsibilities:\n1. Customer Discovery & Technical Scoping\nWork directly with customer engineering, product, business, and domain teams to understand workflows, technical constraints, and high-value AI opportunities.\nTranslate ambiguous customer problems into clear technical plans, success criteria, and delivery milestones.\nIdentify where models can deliver measurable value in real production workflows.\n2. Solution Design & Architecture\nDesign AI-powered systems that integrate models with customer data, tools, APIs, applications, and security controls.\nDefine practical architecture for model usage, retrieval, context management, tool calling, orchestration, evaluation, monitoring, and production reliability.\nBalance speed, quality, safety, cost, scalability, and maintainability.\n3. Hands-On Build & Integration\nBuild prototypes, production applications, APIs, integrations, internal tools, and workflow automation using models.\nWork closely with customer engineering teams to connect AI systems into existing enterprise platforms, data sources, identity systems, and business processes.\nWrite reliable, maintainable code while moving quickly through evolving requirements.\n4. Production Deployment & Adoption\nOwn the path from prototype to production, including testing, rollout planning, observability, reliability, and operational readiness.\nEnsure deployed systems are secure, usable, measurable, and aligned with customer success criteria.\nDrive adoption by working with users, operators, engineering teams, and leadership.\n5. Evaluation, Safety & Reliability\nDefine evaluation methods to measure model quality, grounding, accuracy, latency, cost, safety, and workflow impact.\nBuild feedback loops that detect failures, improve outputs, reduce hallucinations, and maintain trust in production usage.\nEnsure deployments follow security, privacy, access control, compliance, and responsible AI expectations.\n6. Product & Research Feedback\nCapture learnings from real customer deployments and share actionable feedback with Product, Research, Engineering, Safety, and GTM teams.\nIdentify repeatable deployment patterns, product gaps, and opportunities to improve models and platforms.\nHelp turn successful customer solutions into reusable technical patterns and deployment playbooks.\nRequirements\nRequired Qualifications:\nStrong experience in software engineering, applied AI engineering, product engineering, solutions engineering, platform engineering, or technical consulting.\nStrong hands-on programming experience with Python and at least one additional language such as TypeScript, JavaScript, Go, Java, C++, or Rust.\nExperience building production software systems, APIs, integrations, backend services, data pipelines, or customer-facing applications.\nStrong understanding of LLM application patterns such as prompts, context windows, RAG, embeddings, tool/function calling, agents, evaluations, and model orchestration.\nAbility to work directly with customer engineering and business teams in ambiguous, fast-moving environments.\nStrong system design skills with practical judgment around reliability, security, scalability, latency, cost, and maintainability.\nExcellent communication skills with the ability to explain complex technical ideas clearly to technical and non-technical stakeholders.\nOwnership mindset with the ability to move from problem discovery to shipped production outcomes.\nPreferred Qualifications:\nExperience deploying LLM, GenAI, agentic, or AI assistant systems in production.\nExperience with OpenAI API, ChatGPT Enterprise, Codex, or similar AI platforms.\nExperience with retrieval systems, vector databases, workflow automation, enterprise integrations, observability, and evaluation frameworks.\nExperience working in customer-facing engineering roles such as Forward Deployment Engineer, Solutions Engineer, AI Deployment Engineer, Technical Lead, or Founding Engineer.\nExperience deploying AI solutions in complex enterprise environments such as financial services, healthcare, government, legal, customer operations, software engineering, or enterprise productivity.\nExperience turning repeated deployment learnings into reusable platform patterns, product feedback, or internal engineering playbooks.\nTechnical Skill Areas:\nAI Applications: LLMs, RAG, agents, tool calling, prompt design, context engineering, evaluations\nSoftware Engineering: Python, TypeScript, APIs, backend services, integrations, workflow automation\nDeployment: production rollout, observability, reliability, testing, monitoring, incident readiness\nData & Systems: databases, vector search, enterprise APIs, authentication, permissions, data pipelines\nCloud & Platform: Docker, Kubernetes, CI/CD, cloud platforms, serverless, infrastructure basics\nSecurity & Governance: access control, privacy, compliance, auditability, safe model deployment\n Candidate Profile:\nThe 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.\nThey should be comfortable writing code, designing systems, working with executives, partnering with engineers, handling ambiguity, and making practical trade-offs under real delivery pressure.\nThis 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.","description_format":"text","description_chars":7367,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","IT Consulting 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