Forward Deployed Engineer I
Posted 6 days ago
The role in short
Embed with customers to scope, implement, and maintain production data integrations, risk screening workflows, and AI agents on the Sayari 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.
The Role
Sayari’s customers run risk screening, graph analytics and investigation workflows, and data integrations (APIs and feeds) on the Commercial World Model. Those deployments have to work on day one and keep working. As a Forward Deployed Engineer I, you own that outcome. You scope the problem with the customer, prove it technically, ship it, and keep the account healthy after go-live. The same engineer scopes the work and delivers it.
This is not a ticket queue. You receive direction, not a script. You work across a portfolio of accounts rather than embedding in one, so the role rewards engineers who move fast, document as they go, and leave every deployment in a state another engineer can pick up. Opportunity to contribute to Sayari’s agentic platform.
What You’ll Do
- Scope and prove: run technical scoping and evaluation with customer teams, and confirm Sayari fits the workflow before delivery starts.
- Implement: deploy risk screening, graph analytics and investigation workflows, and data integrations (APIs and feeds). Write production Python and SQL to do it.
- Deliver data: execute bulk data cuts and formatted data products (Parquet, CSV), set up and support API endpoints, and onboard customer pipelines: environment setup, data validation, integration testing.
- Keep deployments healthy: monitor usage and pipeline status, build dashboards that give customers and internal teams clear visibility, and resolve issues before they become churn risk.
- Build the playbook: follow and improve delivery playbooks and SOPs. Document your work clearly enough that another engineer can pick it up without a handoff call.
- Feed the product: bring recurring customer needs and field findings back to core engineering.
What We’re Looking For
Required
- 3 to 5 years in a client-facing technical role (forward deployed engineering, sales engineering, technical delivery, or technical consulting).
- Production Python, including the data manipulation ecosystem (Pandas).
- Deep SQL: writing and optimizing complex queries.
- Hands-on experience integrating, testing, and supporting REST APIs.
- Large-scale data processing with Spark, BigQuery, or Snowflake, and pipeline orchestration with Airflow or similar.
- Working knowledge of AWS or GCP.
- AI-assisted coding tools (Cursor, Claude, or similar) as a daily part of how you work.
- Strong written and verbal communication. You explain technical work clearly to engineers, analysts, and senior stakeholders.
- Problem solving in ambiguity, and fast uptake of new technologies and tools.
- Bachelor’s or Master’s degree in a technical field.
Desired
- Building with LLM APIs for analytical or automation tasks.
- Containers (Docker, Kubernetes) and cloud deployment patterns.
- BI and dashboarding tools (Looker, Tableau, Metabase).
- 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 are self-directed. You do not need someone to define every step. You spot gaps, ask the right questions, flag tradeoffs, and execute. You hold a high bar on output quality because the work you hand off represents Sayari to the customer. You take full accountability for what you deliver.
Logistics
- Travel and in-office time: 25-30% 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.
- 1Discovery and scoping
They wrote: “Scope and prove: run technical scoping and evaluation with customer teams, and confirm Sayari fits the workflow before delivery starts.”
A customer says “we want AI in our claims process.” What do you ask in the first meeting?
How to answer
Why they ask: Most FDE work starts this vague. They want to hear you find the real problem before you pick a tool.
- Map the current process: who touches a claim, in what order, with which systems
- Find the slowest or most error-prone step and ask what it costs them
- Agree on one measurable goal (for example time per claim, or error rate)
- Ask what data you can access, and who signs off on security
Tip: Ask about the work, not the technology. Who does it today, how long it takes, what a mistake costs, and how they'd know it got better.
- 2Integrations and APIs
They wrote: “Implement: deploy risk screening, graph analytics and investigation workflows, and data integrations (APIs and feeds). Write production Python and SQL to do it.”
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.
- 3Cloud, deployment and security
They wrote: “Containers (Docker, Kubernetes) and cloud deployment patterns.”
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.
- 4LLMs, RAG and agents
They wrote: “Building with LLM APIs for analytical or automation tasks.”
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.
- 5Coding
They wrote: “Production Python, including the data manipulation ecosystem (Pandas).”
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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