Senior Applied AI Engineer

🔥 0 minutes ago

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

💵 $160k - $190k / year

⏰ Full Time

🟠 Senior

🤖 AI Engineer

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Tango

WebsiteLinkedInAll Job Openings

201 - 500 employees

🏗️ Construction

📦 Logistics

💼 Consulting

Construction • Logistics • Consulting

Tango is a connected software company that provides an intelligence platform for managing commercial real estate across the full property lifecycle. Its SaaS products support site selection, portfolio strategy, lease and transaction management, projects & maintenance, workplace optimization, and energy & sustainability, using predictive analytics and AI to deliver actionable insights. Tango serves enterprises, retailers, landlords, and government/education facilities to reduce costs, improve utilization, and streamline ESG reporting.

📋 Description

• Build and ship Tango’s first AI-powered product • Design, build, and ship production AI agents on LangGraph • Own agents end to end, including graph design, tool definitions, prompt and context engineering, durable execution, failure and retry behavior, and cost and latency budgets • Build agent tooling against internal systems over MCP, using direct API calls and agent-to-agent interfaces where appropriate • Deliver human-in-the-loop review flows with interrupt points, confidence surfacing, and correction paths • Build and tune retrieval covering chunking, hybrid retrieval, grounding, and citations back to source page and paragraph • Contribute golden datasets, LLM-as-judge and deterministic scorers, and CI regression suites • Diagnose quality failures and correct the appropriate layer • Own agent-level safety behavior, including prompt-injection resistance, PII handling, refusal, and escalation paths • Partner with Product to translate accuracy thresholds, confidence disclosure, and human-in-the-loop triggers into shipped behavior • Work with Platform Engineering on deployment and Data Platform on curated datasets • Feed agent session and usage analytics into the warehouse • Transition reference agents to domain teams and contribute to the shared agent quality standard

🎯 Requirements

• 7+ years of professional software engineering experience • 2+ years building LLM-powered systems that reached production and real users • Strong expertise with Python and its service stack, including FastAPI, Pydantic, or equivalents • Engineering discipline in testing, code review, CI/CD, and production ownership • Production experience with an agent orchestration framework • Hands-on depth with at least one frontier model API • Hands-on experience with LLM evaluation, including golden datasets, LLM-as-judge, deterministic scorers, regression testing, and release gating • Experience with MCP tool servers or comparable tool and function-calling protocols • Experience with multi-agent patterns • Production RAG and retrieval experience, including chunking strategy, hybrid retrieval, grounding, citation, and diagnosing retrieval failures • Experience with LLM observability and tracing, such as LangSmith, Langfuse, Arize, or equivalent • Experience with prompt and version management • Sound judgment about hallucination, prompt injection, silent degradation, and acceptable versus unacceptable failures • LangGraph strongly preferred; LangChain, OpenAI or Claude Agents SDKs, or equivalent frameworks considered • Preferred experience with graph-backed agent memory or knowledge graphs • Preferred experience with production vector and hybrid retrieval stores • Preferred experience with async task orchestration for long-running document pipelines • Preferred experience with document intelligence and information extraction at scale, including OCR, layout-aware parsing, and structured extraction from long documents • Applicants must be authorized to work in the U.S. for any employer • Tango cannot sponsor employment-based visas at this time

🏖️ Benefits

• Health, dental, and vision insurance • 401(k) plan with company match • Generous paid time off • Fully remote work arrangement • Inclusive and collaborative culture • Competitive compensation