Forward-Deployed AI Engineer

Job Summary

Forward-Deployed AI Engineer

Location: India

Reports To: Snr. Director of AI and Data, Enterprise Data Services (UK)

Level: Individual Contributor (3-4 years experience)

Team: Enterprise Data Services - AI Engineering

Contract: Permanent, Full-Time

Role Overview

Were looking for an 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 wont be designing schemas or managing pipelines. Youll 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 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

Requirements

Must Have

• 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

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