Junior Forward-Deployed AI Engineer (LLM/ML) at SilentEight

Capon Bridge, West Virginia, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Problem-Solving, Communication, LLMs, ML, Python, SQL, Evaluation, Experimentation, FastAPI, Docker, Entity Resolution, Link Analysis, Pattern-Of-Life, Risk Identification, Explainability, Auditability

Industry

technology;Information and Internet

Description

At Silent Eight, we develop our own AI-based products to combat financial crimes that enable things like money laundering, the financing of terrorism, and systemic corruption. We’re a leading RegTech firm working with large international financial institutions such as Standard Chartered Bank and HSBC. Join us and help make the world a safer place! We solve hard, real‑world problems — from uncovering financial crime, fraud patterns and mule networks, through prioritising thousands of alerts, to crafting defendable case narratives. We work close to users (analysts, investigators, risk/compliance), iterate fast, and deliver in weeks, not quarters. The adversary adapts — this is an intelligence game, not an academic benchmark. The Role We’re looking for someone who solves business problems with technology. Less stack worship, more outcomes: fast risk identification, fewer false positives, faster time‑to‑decision, better explainability, and lower cost per case. Finance shows up often, but we think broader — investigations, decisions and client value across industries. What you’ll do • Go to the field: talk to users, shadow their workflows, capture the as‑is → goals & constraints. • Define hypotheses & KPIs (precision/recall, FPR, TAT, coverage, cost/decision) and turn them into experiment plans. • Design decision flows that mix LLMs, retrieval/RAG, classical ML, and lightweight rules; ensure explainability and auditability. • Build quick prototypes (notebook → lightweight service/API) and measure their impact on real data. • Create evaluation sets and scoring rubrics (offline + side‑by‑side + sanity checks + guardrails). • Present findings & recommendations directly to decision‑makers; propose rollout (pilot → production‑lite → scale). • Lead innovation processes across the company; test, promote solutions and mentor others with new AI technologies. Minimum Requirements • Problem‑solving & communication: you can break down fuzzy problems and explain risks to non‑technical stakeholders. • LLMs + ML in practice: RAG, prompting, tool‑calling; classification/ranking/deduplication; fundamentals of evaluation & experimentation. • Python + SQL sufficient to build a prototype that works and can be maintained. • Polish & English fluency for user conversations and concise write‑ups. Nice to Have • A track record of delivery: 2–3 examples where your AI/ML solution materially improved process KPIs (any industry). • Experience with investigations / trust & safety / fraud / risk / audit or other complex decision processes. • Graphs/ER: entity resolution, link analysis, pattern‑of‑life. • Light engineering craft: FastAPI, Docker; the rest (K8s/CI/CD/Terraform) is not required. Our Tech (lightweight) We don’t fetishise the stack. Common tools: Python, SQL, notebooks/analysis tools, lightweight APIs (e.g., FastAPI), simple stores (e.g., Postgres), and vector indexes. We choose tools pragmatically — business impact beats heavy infrastructure Note: we don’t expect mastery of “every” tool. What matters are strong fundamentals, curiosity, and a habit of delivering measurable outcomes.

Responsibilities

The role involves solving business problems with technology, focusing on fast risk identification and better decision-making. You will engage with users to understand their workflows and design decision flows that incorporate various AI technologies.