Senior Applied AI Engineer

Locatee AG; United States; Hybrid; USD 160,000 - 190,000; Full time

Benefits offered by this job

Competitive compensation

Comprehensive benefits

Flexible work environment

Inclusive culture

Job summary

Tango Analytics is seeking a Senior Applied AI Engineer to help build and ship Tango’s first AI-powered product. You will turn advances in generative AI and machine learning into reliable capabilities that solve meaningful problems for our customers, collaborating with Product, Engineering, and domain experts to identify high-value use cases and take ideas to production.

You’ll influence technical architecture, engineering standards, evaluation practices, and product strategy while tackling

Qualifications

• 7+ years of professional software engineering experience, including 2+ years building LLM-powered systems that reached production and real users.
• Strong expertise with Python and its service stack (FastAPI, Pydantic, or equivalents), along with the engineering discipline that supports it: testing, code review, CI/CD, and production ownership.
• Production experience with an agent orchestration framework. LangGraph strongly preferred; LangChain, OpenAI or Claude Agents SDKs, or equivalent frameworks considered.
• Hands-on depth with at least one frontier model API.
• Hands-on experience with LLM evaluation: golden datasets, LLM-as-judge and deterministic scorers, regression testing, and using evaluation results to gate releases.
• Experience with MCP tool servers or comparable tool and function-calling protocols, and 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 (LangSmith, Langfuse, Arize, or equivalent), and with prompt and version management.
• Sound judgment about failure modes such as hallucination, prompt injection, and silent degradation, with the ability to distinguish acceptable failures from unacceptable ones.

Responsibilities

• Design, build, and ship production AI agents on LangGraph, keeping the agent layer portable across cloud platforms.
• Own agents end to end: 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 where those are the better fit, and agent-to-agent interfaces as agents begin to compose.
• Deliver human-in-the-loop review flows, including interrupt points, confidence surfacing, and the correction paths that let a customer review and override agent output.
• Build and tune retrieval, covering chunking, hybrid retrieval, grounding, and citation back to source page and paragraph.
• Contribute the evaluations for the agents you ship: golden datasets, LLM-as-judge and deterministic scorers, and regression suites that run in CI, built against the shared evaluation harness the platform provides.
• Diagnose quality failures to root cause, whether retrieval miss, prompt defect, tool error, model regression, or flawed ground truth, and correct the appropriate layer.
• Own agent-level safety behavior, including prompt-injection resistance, PII handling, and refusal and escalation paths, applying the platform guardrail service maintained by Platform Engineering.
• 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 with Data Platform on the curated datasets agents read.
• Feed curated agent session and usage analytics into the warehouse so agent performance is measurable alongside product analytics.
• Transition reference agents to the domain teams that will operate them long-term, and contribute to the shared agent quality standard.

Skills

Python

LLM-powered systems

CI/CD

LangGraph

LangChain

OpenAI API

Prompt engineering

Multi-agent systems

Retrieval augmented generation

Tools

FastAPI

Pydantic

LangSmith

Langfuse

Arize

Neo4j

pgvector

Pinecone

Weaviate

Job description

Let’sTango!WhereInnovationMeetsImpact.

At Tango Analytics, we’re all about helping businesses make smarter decisions through powerful technology, insightful data, and a whole lot of collaboration. Whether you're a creative thinker, a strategic planner, a tech wizard, or a customer champion, there's a place for you on our team. We believe work should be meaningful and fun - so if you're ready to make a difference while enjoying the journey, come join us and let's Tango!

RoleSummary:

We are looking for a Senior Applied AI Engineer to help build and ship Tango’s first AI-powered product, marking an important next chapter after 18 years as an industry leader in real estate technology.

In this highly hands-on role, you will turn advances in generative AI and machine learning into reliable, production-ready capabilities that solve meaningful problems for our customers. You’ll work closely with Product, Engineering, and domain experts to identify high-value use cases, rapidly prototype solutions, evaluate approaches, and take the best ideas all the way into production.

This is an opportunity to help establish how AI is built and applied at Tango from the ground up. You’ll influence technical architecture, engineering standards, evaluation practices, and product strategy while tackling complex, data-rich workflows across corporate real estate, workplace management, lease administration, and retail location management.

Key Responsibilities:

Agent Design & Delivery

Design, build, and ship production AI agents on LangGraph, keeping the agent layer portable across cloud platforms.

Own agents end to end: 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 where those are the better fit, and agent-to-agent interfaces as agents begin to compose.

Deliver human-in-the-loop review flows, including interrupt points, confidence surfacing, and the correction paths that let a customer review and override agent output.

Build and tune retrieval, covering chunking, hybrid retrieval, grounding, and citation back to source page and paragraph.

Evaluation, Quality & Trust

Contribute the evaluations for the agents you ship: golden datasets, LLM-as-judge and deterministic scorers, and regression suites that run in CI, built against the shared evaluation harness the platform provides.

Diagnose quality failures to root cause, whether retrieval miss, prompt defect, tool error, model regression, or flawed ground truth, and correct the appropriate layer.

Own agent-level safety behavior, including prompt-injection resistance, PII handling, and refusal and escalation paths, applying the platform guardrail service maintained by Platform Engineering.

Cross-Functional Collaboration

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 with Data Platform on the curated datasets agents read.

Feed curated agent session and usage analytics into the warehouse so agent performance is measurable alongside product analytics.

• Transition reference agents to the domain teams that will operate them long-term, and contribute to the shared agent quality standard.

Required Skills:

7+ years of professional software engineering experience, including 2+ years building LLM-powered systems that reached production and real users.

Strong expertise with Python and its service stack (FastAPI, Pydantic, or equivalents), along with the engineering discipline that supports it: testing, code review, CI/CD, and production ownership.

Production experience with an agent orchestration framework. LangGraph strongly preferred; LangChain, OpenAI or Claude Agents SDKs, or equivalent frameworks considered.

Hands-on depth with at least one frontier model API.

Hands-on experience with LLM evaluation: golden datasets, LLM-as-judge and deterministic scorers, regression testing, and using evaluation results to gate releases. This is central to the role.

Experience with MCP tool servers or comparable tool and function-calling protocols, and 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 (LangSmith, Langfuse, Arize, or equivalent), and with prompt and version management.

Sound judgment about failure modes such as hallucination, prompt injection, and silent degradation, with the ability to distinguish acceptable failures from unacceptable ones.

Preferred:

Graph-backed agent memory or knowledge graphs (Neo4j or similar).

Production vector and hybrid retrieval stores (pgvector, Pinecone, Weaviate, Qdrant).

Async task orchestration for long-running document pipelines (Celery/Redis or equivalent).

Document intelligence and information extraction at scale, including OCR, layout-aware parsing, and structured extraction from long documents.

What We Offer

We’recommittedtocreatinganenvironmentwhereyoucanthrive- professionallyandpersonally.Ourofferingsinclude:

• CompetitiveCompensationWerecognizeandrewardyourcontributionswithasalarypackagethatreflectsyourvalue.
• ComprehensiveBenefitsIncludinghealth,dental,andvisioninsurance,a401(k)planwithcompanymatch,andgenerouspaidtimeofftosupportyourwell-being.
• FlexibleWorkEnvironmentWhetherremote,hybrid,orin-office,wesupportworkarrangementsthatpromoteproductivityandbalance.
• Inclusive&CollaborativeCultureWefosteraworkplacewherediverseperspectivesarevalued,teamworkisencouraged,andeveryonehasavoice.

Tango is proud to be an equal opportunity employer. We are committed to equal opportunity regardless of race, ethnicity, religion, parental status, sexual orientation, age, citizenship, disability, or veteran status.

Basepayofferediscontingentonqualificationsandotheroperationalconsiderations.BasepayisjustonepieceofthefullcompensationstructureofferedatTango.Ifthispayrangeisoutsideofyourexpectations,westillencourageyoutoapplyandhaveaconversationwithus.

Base pay offered for this position is: $160,000 - $190,000

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