Junior Forward-Deployed AI Engineer (LLM/ML)

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.

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.

Originally posted on Himalayas