Forward-Deployed AI Engineer

Why Choose Bottomline?

Are you ready to transform the way businesses pay and get paid? Bottomline is a global leader in business payments and cash management, with over 35 years of experience and moving more than $16 trillion in payments annually. We're looking for passionate individuals to join our team and help drive impactful results for our customers. If you're dedicated to delighting customers and promoting growth and innovation - we want you on our team!

Job Description: Forward-Deployed AI Engineer

Location

India (Pune / Bangalore / Remote 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

HRFs

BTF27272, BTF27273

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, 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 Engineer s 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 F intech, 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 b usiness 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 s : 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

We welcome talent at all career stages and are dedicated to understanding and supporting additional needs. We're proud to be an equal opportunity employer, committed to creating an inclusive and open environment for everyone.