Forward-Deployed AI Engineer

Overview

Forward-Deployed AI Engineer on Bottomline's Enterprise Data Services AI Engineering team, turning business problems into production AI solutions built natively in Snowflake.

What you'll 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.
• Present findings and recommendations to business stakeholders in plain language.
• Contribute to the AI project lifecycle: framing problems, running spikes, evaluating results, and iterating.

What you'll need

• 3-4 years experience in data science, machine learning, or applied AI.
• Strong Python (pandas, scikit-learn, numpy).
• 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, including missing values, inconsistent formats, and multiple sources.
• Great communication skills; can work directly with stakeholders end to end.
• Can communicate findings to non-technical stakeholders clearly.

Nice to have

• 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 (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.

Details

• Location: India (Pune / Bangalore / Remote India).
• Reports to: Snr. Director of AI and Data, Enterprise Data Services (UK).
• Level: Individual Contributor.
• Employment: Permanent, Full-Time.