{"id":21194,"url":"https://alion.io/job/infinity-senior-ai-engineer-core-supernal","title":"Senior AI Engineer (Core) - Supernal","company":{"id":6015,"name":"Infinity Constellation","domain":"infinityconstellation.com","url":"https://alion.io/company/infinity","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"D","score":40,"open_postings":11,"ghost_share":0.909,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":75,"computed_at":"2026-10-21T05:46:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":{"label":"US time zones","utc_offset_min":-8,"utc_offset_max":-5},"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":144000,"max_usd":265000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":338},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"Chroma","optional":false},{"name":"CrewAI","optional":false},{"name":"Datadog","optional":false},{"name":"Django","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Text-to-Speech","optional":false},{"name":"Voice Agents","optional":false},{"name":"Weaviate","optional":false},{"name":"WebRTC","optional":false},{"name":"Python","optional":true}],"status":"closed","first_seen_at":"2026-04-16T01:54:00Z","employer_posted_date":"2026-04-16","last_verified_at":"2026-10-21T21:31:46Z","board_verified":false,"closed_at":"2026-10-21T21:31:46Z","days_open":188,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":188},"description":"Senior AI Engineer\nAbout Supernal\nSupernal helps small-to-medium businesses hire their first AI employee. Our AI teammates are built using intelligent, agentic workflows deployed on a proprietary platform. We deliver working, value-generating AI Employees-not tools-that handle real business processes alongside human teams.\nThe Role\nWe’re hiring a Senior AI Engineer to build and ship the first generation of personalized, self-improving agentic workflows that users rely on daily. This is an “end-to-end” role: you’ll design the agent runtime, memory + retrieval systems, evaluation harnesses, and the product-facing surfaces that put agents in front of real users at scale.\nYou should be equally comfortable reasoning about distributed systems and data (latency, caching, queues, failure modes, cost) as you are with modern agent stacks (tool use, memory, RAG, multi-step planning, guardrails, and evaluation).\nThis role will partner closely with platform engineering to leverage and extend our core services (Django backend, event-driven systems, Kubernetes, observability) while owning critical parts of the AI application layer.\nWhat You’ll Build\nPersonalized agent runtime: Agentic workflows that adapt to a user’s preferences, data, and ongoing behavior over time.\n\nMemory & retrieval systems: Short/long-term memory, durable state, and retrieval pipelines across vector DBs and relational data.\n\nVoice experiences (real-time + async): Speech-to-speech/voice agents, streaming audio pipelines, turn-taking, interruption handling, latency tuning, and QA for natural conversations.\n\nAgent evaluation + reliability: Offline/online evals, regression suites, red-teaming, monitoring, and rollout controls so agents are trustworthy in production.\n\nProduction agent infrastructure: Scalable orchestration patterns for multi-step jobs, background tasks, and user-facing interactions (sync + async), with clear SLAs/SLOs.\n\nTooling + developer experience: Libraries and primitives that make it easy for the team to build new agent capabilities quickly and safely.\n\nWhat You’ll Own (Responsibilities)\nShip user-facing agent experiences end-to-end: prototype → production → iteration based on real usage.\n\nArchitect and implement stateful agent systems (workflows, tool calling, memory, retrieval, and human-in-the-loop where needed).\n\nBuild voice features end-to-end where they unlock value: realtime speech agents, voice UI/UX, prompt/audio routing, and guardrails for safe tool execution.\n\nBuild/own an evaluation harness:\ncurated test sets + scenario suites\n\nautomated scoring / rubric-based graders\n\nprompt/model/version tracking\n\ncanary + A/B experimentation and safe rollout patterns\n\nDesign data + retrieval pipelines:\nchunking, enrichment, metadata strategy\n\nhybrid retrieval (vector + keyword + structured filters)\n\nre-ranking, caching, and latency optimization\n\nmulti-tenant safety and data isolation\n\nIntegrate with and extend our platform primitives:\nDjango/DRF/ASGI services\n\nasync execution + queues + workflow orchestration\n\nPostgreSQL + pgvector\n\nKubernetes deployments, autoscaling, and cost controls\n\nEstablish engineering rigor for agents:\nobservability (traces, spans, structured logs)\n\nreliability patterns (timeouts, retries, circuit breakers, graceful degradation)\n\nsecurity/privacy controls for data access and tool execution\n\nWhat We’re Looking For\nRequired\nStrong software engineering fundamentals (design, testing, code quality, performance, security).\n\nProduction experience deploying AI systems in front of users (not just notebooks/demos).\n\nExperience building agentic or LLM-powered systems with memory and tool use.\n\nComfort working across application + infrastructure layers: APIs, background jobs, data stores, and deployment.\n\nHands-on experience with at least one agent framework (or equivalent custom implementation), such as:\nLangChain / LangGraph\n\nLlamaIndex\n\nAutoGen / CrewAI-style multi-agent patterns\n\nStrong understanding of retrieval and vector search concepts: embeddings, indexing, filtering, evaluation.\n\nPreferred\nExperience with vector databases and/or search stacks (e.g., Pinecone, Chroma, Weaviate, Qdrant, pgvector).\n\nExperience designing evaluation systems (offline eval, human eval loops, production monitoring, prompt/model regression).\n\nExperience building voice/real-time systems (streaming, WebRTC or similar), and/or integrating speech (STT/TTS) into production applications.\n\nExperience building durable, long-running workflows (Temporal or similar orchestration engines).\n\nFamiliarity with observability tooling (OpenTelemetry, Datadog, or similar).\n\nExperience shipping multi-tenant SaaS systems with strong privacy boundaries.\n\nInterview Focus Areas\nSystem design for agentic applications (state, memory, evaluation, failure modes).\n\nPractical retrieval/RAG design (data modeling, indexing, relevance, latency).\n\nProduction engineering practices (testing strategy, observability, rollouts).\n\nAbility to communicate tradeoffs and make good technical decisions under uncertainty.\n\nCompensation & Logistics\nCompensation: Competitive salary commensurate with experience (Senior level)\n\nLocation: Remote\n\nType: Full-time\n\nRequirements: Overlap with Americas timezones for collaboration; 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