[Hiring] Forward-Deployed AI Engineer @Bottomline

Role Description

We're looking for a Forward-Deployed AI Engineer (FDE) who can take a business problem and solve it natively in Snowflake — from feature engineering through model training to deployment as a live service. This is not a platform-building role. You won't be designing schemas or managing pipelines. You'll be the person who turns business questions into working AI solutions that run in production.

What You Will Do

• Take business problems (e.g. churn risk, propensity to buy, anomaly detection, document intelligence) and design AI solutions using Snowflake-native capabilities.
• Build and evaluate ML models using Snowflake ML Functions, Snowpark ML, and Cortex AI.
• Design and implement Cortex Agents that combine structured data queries with unstructured document intelligence.
• Create evaluation frameworks (golden datasets, LLM-as-judge, human review loops) to prove solutions meet success metrics.
• Work with Cortex Analyst semantic views to enable natural language access to business data.
• Build Cortex Search services over unstructured documents (contracts, support tickets, product docs, etc.).
• Collaborate with AI and Data Engineers who handle platform infrastructure; you focus on the science, they handle the plumbing.
• Present findings and recommendations to business stakeholders in plain language.
• Contribute to the AI project lifecycle: framing problems, running spikes, evaluating results, and iterating.

Qualifications

• 3-4 years experience in data science, machine learning, or applied AI.
• Strong Python (pandas, scikit-learn, numpy), you think in DataFrames.
• SQL fluency, comfortable writing complex analytical queries, window functions, CTEs.
• Experience building classification or regression models and evaluating them rigorously (precision, recall, F1, AUC).
• Experience with at least one of: NLP, document extraction, recommendation systems, or anomaly detection.
• Comfortable working with messy real-world data — missing values, inconsistent formats, multiple sources.
• Great communication skills and can work directly with stakeholders end to end.
• Can communicate findings to non-technical stakeholders clearly.

Requirements

• Strong Preference:
• Snowflake experience (Snowpark Python, Cortex AI functions, ML model registry).
• Experience with LLMs in production (prompt engineering, RAG architectures, evaluation of generative outputs).
• Understanding of Cortex Agents, Cortex Search, or Cortex Analyst.
• Experience building evaluation sets and measuring AI quality systematically (not just accuracy, precision/recall tradeoffs, error analysis).
• Familiarity with feature engineering for tabular business data (customer signals, transaction patterns, contract attributes, etc.).
• Nice to Have:
• Experience in Fintech, B2B payments, or B2B SaaS.
• Familiarity with A/B testing or causal inference methods.
• Experience with Streamlit for rapid prototyping of data apps.

Benefits

• AI-native, not AI-assisted: You will build solutions where the AI IS the product, not a feature bolted onto something else.
• No ML infrastructure tax: Everything runs natively in Snowflake — no Docker, no Kubernetes, no model serving endpoints to manage.
• You will have business impact from day one: with a prioritised backlog of Quick Win candidates waiting for someone to build them.
• Direct line to stakeholders: No layers of middle management between you and the business problem.
• Modern AI engineering practices: Lifecycle-governed (Framing > Spikes > Build > Evaluate > Governance > Adoption), evaluation-driven, with a real governance plane.

Your First 90 Days

• Days 1-30: Learn - Understand the Snowflake platform and Cortex AI capabilities; run through the AI project lifecycle on a small candidate.
• Days 30-60: Spike - Own your first AI candidate from the prioritised backlog; run a timeboxed spike: build the crudest version that proves positive signal; create your first golden evaluation dataset; present spike findings to the team.
• Days 60-90: Build - Build your spike to production standard with proper evaluation; pass through governance gates; ship your first AI solution to production.