Bottomline - Forward-Deployed AI Engineer
Requirements
• 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 communications skills and can work directly with stakeholders end to end • Can communicate findings to non-technical stakeholders clearly • Strong Preference • 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) • 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 • Snowflake (everything lives here): Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, Snowpark Python, ML Functions, Dynamic Tables • Python: pandas, scikit-learn, snowpark-ml, evaluation frameworks • Version control: Git, Snowflake stage-based deployment • Visualisation: Streamlit in Snowflake, Cortex Code (IDE), PowerBI
Responsibilities
• 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 handles 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
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 • Your First 90 Days • Days 1-30: Learn • 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 • Days 30-60: Spike • Own your first AI candidate from the prioritised backlog • Run a timeboxed spikes: build the crudest version that proves positive signal • Create your first golden evaluation dataset • Present spike findings to the team • Days 60-90: Build • Days 60-90: Build • Build your spike to production standard with proper evaluation • Pass through governance gates • Ship your first AI solution to production
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