Forward Deployed Engineer II
Posted 3 days ago
The role in short
Embed with government and enterprise customers to build, deploy, and maintain production AI agents and integrations on Sayari's agentic platform.
About Sayari:
Sayari is the judgment infrastructure for trustworthy AI in economic security and commercial risk. The Sayari Commercial World Model resolves 11.7B+ primary-source records from 250+ jurisdictions forming the ground truth of global commerce. A Judgment Ontology, encoding over a decade of investigative tradecraft, and Superconductor, an agentic orchestration platform, deliver AI that reasons like an expert analyst, shows its work, and traces every finding to its source. Trusted by U.S. Customs and Border Protection, HM Revenue & Customs, and Fortune 500 enterprises, Sayari is used by thousands of professionals across 35+ countries to secure supply chains and dismantle illicit networks. Headquartered in Washington, D.C., with offices in London, Singapore, Tokyo, and Tel Aviv.
Forward Deployed Engineer II
The Role
Sayari’s largest customers, the biggest government agencies and Fortune 100 companies, are moving from data products to an agentic platform that runs their investigation and risk workflows end to end. As a Forward Deployed Engineer II, you are dedicated to those accounts. You own the technical win (demo, proof of concept, trial), then the deployment, then what comes after. The same engineer scopes the problem and ships it.
You are equal parts engineer, strategist, and operator. You embed in the customer’s environment, build on Sayari’s agentic platform against their data and their workflows, and solve high-stakes problems that have no textbook answers. You are the conduit between the front lines and core engineering: what you learn in the field becomes what the platform does next.
What You’ll Do
- Win the technical evaluation: design and run demos, proofs of concept, and trials that prove the platform on the customer’s own data. Define success criteria with the customer and hit them.
- Deploy the agentic platform: build agents, tools, and integrations on Sayari’s agentic platform inside the customer’s environment, and take them from trial to production.
- Embed and build: ship production Python, SQL, and integrations into customer persistence layers and analytics stacks.
- Own the relationship: serve as the primary technical authority for the account. Brief executives, analysts, and mission owners on outcomes.
- Triage and feed engineering: decide what is bespoke, what is reusable, and what belongs in the platform. Document capability gaps and drive them into the roadmap.
- Raise the bar: set delivery standards and review work for other engineers on your accounts.
What We’re Looking For
Required
- 5+ years in a client-facing technical role (forward deployed engineering, sales engineering, or technical consulting).
- 7-8+ total years of experience working in software engineering and/or solutions consulting
- A record of owning strategic deployments end to end, from technical win to production.
- Production Python, including the data manipulation ecosystem (Pandas). Deep experience having worked with SQL. Hands-on experience integrating and reasoning about REST APIs.
- Building and deploying on cloud platforms (AWS, GCP, or Azure) with containers (Docker, Kubernetes).
- You are a technology polyglot, you have worked across multiple tech stacks and enjoy getting the opportunity to navigate different coding languages, frameworks, and systems.
- AI-assisted coding tools (Cursor, Claude, or similar) as a daily part of how you work.
- Executive communication. You articulate technical concepts with clarity and conviction to engineers, analysts, and senior leaders alike.
- Problem solving in ambiguity, and the drive to parachute into new environments and learn new technologies on the fly.
- Bachelor’s or Master’s degree in a technical field.
Desired
- Hands-on experience building agentic or LLM systems: orchestration, tool use, retrieval, evaluation.
- Sovereign, air-gapped, or government cloud deployments.
- Modern data stack (Snowflake, Databricks) and workflow orchestrators (Airflow, Prefect).
- Graph data technologies and their query languages.
- Exposure to ML, NLP, or entity resolution.
- Experience in high-stakes domains: AML and financial crime, supply chain risk, or national security.
How You Work
You operate with extreme ownership. You build the bridge rather than flag the gap. You prefer a working prototype to a slide about one. You can scrap a month of work when the mission changes without losing momentum, and you hold yourself accountable for the outcome, not the activity.
Logistics
- Travel and in-office time: 25 to 30% combined.
- Limitless growth and learning opportunities.
Sayari is an equal opportunity employer and strongly encourages diverse candidates to apply. We believe diversity and inclusion mean our team members should reflect the diversity of the United States. No employee or applicant will face discrimination or harassment based on race, color, ethnicity, religion, age, gender, gender identity or expression, sexual orientation, disability status, veteran status, genetics, or political affiliation. We strongly encourage applicants of all backgrounds to apply.
Pay Range
$160,000—$180,000 USD
Prep for this interview
5 questions likely to come up at Sayari, each matched to a line in this posting.
- 1Cloud, deployment and security
They wrote: “Building and deploying on cloud platforms (AWS, GCP, or Azure) with containers (Docker, Kubernetes).”
How would you debug a deployment that works in staging but fails at the customer's site?
How to answer
Why they ask: Customer environments differ in ways you can't see. This tests method under pressure.
- Compare versions, config and environment variables
- Check network rules, proxies and certificates (common in enterprise)
- Look at resource limits and permissions
- Add the missing check to your install steps afterwards
Tip: List the usual suspects and how you'd check each without full access.
- 2LLMs, RAG and agents
They wrote: “Hands-on experience building agentic or LLM systems: orchestration, tool use, retrieval, evaluation.”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 3Integrations and APIs
They wrote: “Deploy the agentic platform: build agents, tools, and integrations on Sayari’s agentic platform inside the customer’s environment, and take them from trial to production.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 4Data and SQL
They wrote: “Embed and build: ship production Python, SQL, and integrations into customer persistence layers and analytics stacks.”
Write a query that finds each customer's first and most recent order.
How to answer
Why they ask: A quick check that you're comfortable with grouping, joins and window functions.
- Clarify the tables and what counts as an order (cancelled ones?)
- Use MIN/MAX with GROUP BY, or ROW_NUMBER() if you need the whole row
- Handle ties on the same timestamp, and customers with no orders
- Explain how you'd test it on a few known customers
Tip: Say what you'd check in the data before writing the query: duplicates, time zones, nulls.
- 5Coding
They wrote: “Production Python, including the data manipulation ecosystem (Pandas). Deep experience having worked with SQL. Hands-on experience integrating and reasoning about REST APIs.”
Here's a CSV export from a customer. Write a script that cleans it and loads it into our API.
How to answer
Why they ask: Very close to real FDE work. They watch how you handle bad rows and failures.
- Read with a proper CSV parser; check the header first
- Validate each row and collect errors, not crash on the first one
- Send in batches with retries and a clear report at the end
- Write one or two quick tests for the parsing
Tip: Talk through edge cases out loud: encodings, missing fields, duplicates, rate limits.
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