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This is based in Greater Bengaluru Area.

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Forward-Deployed AI Engineer

Bottomline

Greater Bengaluru Area

About the Team

Enterprise Data Services (EDS) is Bottomline's AI engineering function. We don't build AI as a side tool — we build AI-native solutions where intelligence IS the product. Our platform runs on Snowflake, and we use Snowflake's native AI capabilities (Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, ML Functions) to solve business problems directly in the data platform—no external ML infrastructure, no separate serving layer, and no handoffs between teams.

We currently ship: churn prediction agents, contract intelligence, partner reporting automation, a governance control plane, and much more. Everything runs in Snowflake. Everything is versioned, tested, and production-governed.

The Role

We're 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 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 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
Great communications skills and can work directly with stakeholders end to end
Can communicate findings to non-technical stakeholders clearly

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

Our Tech Stack

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

Why This Role Is Different

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

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