{"id":3453641,"url":"https://alion.io/job/next-dialog-forward-deployed-ai-engineer","title":"Forward Deployed AI Engineer","company":{"id":4485867,"name":"Next Dialog","domain":"nextdialog.ai","url":"https://alion.io/company/next-dialog","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Ukraine"],"countries":["UA"],"hiring_countries":["UA"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"LiveKit","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Next.js","optional":false},{"name":"PostgreSQL","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"Anthropic","optional":true},{"name":"Claude Code","optional":true},{"name":"Cursor","optional":true},{"name":"Deepgram","optional":true},{"name":"Docker","optional":true},{"name":"ElevenLabs","optional":true},{"name":"Gemini","optional":true},{"name":"JavaScript","optional":true},{"name":"Langfuse","optional":true},{"name":"Nest.JS","optional":true},{"name":"Node JS","optional":true},{"name":"OpenAI","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Text-to-Speech","optional":true},{"name":"Twilio","optional":true},{"name":"TypeScript","optional":true},{"name":"Vapi","optional":true},{"name":"Vertex AI","optional":true},{"name":"WebRTC","optional":true}],"status":"live","first_seen_at":"2026-10-28T16:25:18Z","employer_posted_date":null,"last_verified_at":"2026-10-28T16:25:18Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"About us\n\nNext Dialog automates business communication with AI. Our omnichannel platform handles customer conversations across phone, SMS and messaging: answering questions, booking appointments, qualifying leads and syncing everything to the CRM, 24/7. Our clients range from HVAC installers and real estate brokerages to dental clinics, logistics and energy companies across Poland, Germany, the rest of Europe and the US. We're a small, product-focused team working at the intersection of AI and communication automation, and we're hiring a Forward Deployed AI Engineer to own our AI systems from architecture to live production.\nThe role\n\nYou own AI systems end to end, from architecture to live production. You will:\n\nBuild harnesses: test and eval harnesses for LLM and voice pipelines, simulated conversations, and regression suites that catch quality drops before clients see them\n\nInstrument what ships: tracing of LLM and agent calls (prompts, tool calls, tokens, cost, latency), quality monitoring and regression alerts in production\n\nDesign and build microservices: new services and integrations that fit into a platform running in production, including MCP servers and tools for our agents and internal tooling\n\nOptimize AI pipelines: latency, cost, accuracy, and reliability, measured with real metrics\n\nAutomate everything repetitive: checks, deployments, agent setup, and routine ops, using scripts, agents, and MCP tooling\n\nDesign for safety and privacy: prompt injection, PII handling, call-recording consent, human approval for irreversible actions\n\nOwn live production: monitor, debug, and fix real campaigns running real calls\nWhat the role is not\n\nNot a research role. No model training from scratch, no TensorFlow/PyTorch.\n\nNot a prompt-only role. Prompts are one tool among evals, tracing, integrations and ops.\n\nNot a frontend role. React/Next.js is in the stack, but it is not where you will spend your days.\nOur stack\n\nTypeScript, NestJS, PostgreSQL, React, Next.js · Retell AI, LiveKit, Deepgram, ElevenLabs, Twilio · Gemini/Vertex AI, OpenAI, Anthropic · Langfuse / OpenTelemetry-style tracing · GCP, Terraform, Docker · Cursor, Claude Code, MCP\nMust have\n\n4+ years of engineering experience in any server-side language, with a strong track record of designing, running and supporting microservice architectures in live production\n\nProduction experience with LLMs: prompting, structured output, tool calling, RAG, evals\n\nExperience building test or eval harnesses and automating pipelines\n\nExperience with LLM observability: tracing prompts, tool calls, tokens, cost and latency, and using traces to find and fix regressions\n\nAI-native workflow: you do most of your tasks through AI agents and know how to build context for them\n\nHands-on experience with MCP: you have built or wired MCP servers and tools, not only used them\n\nStrong SQL: comfortable analysing call and campaign data in PostgreSQL\n\nExperience with real-time or webhook-driven systems\n\nYou design for safety and privacy: prompt injection, PII, consent, guardrails\n\nEnglish B2+\nNice to have\n\nVoice AI experience (Retell, Vapi, LiveKit, Pipecat, telephony, SIP/WebRTC, streaming audio, STT/TTS, turn-taking and interruptions, latency tuning)\n\nPast experience as a team or tech lead\n\nForward deployed or solutions engineering experience at a startup\n\nGCP, Terraform, on-prem deployments\nHiring process\n\nOnline 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