Posted today · be early
Forward-Deployed AI Engineer
Bottomline Technologies
IndiaremotePosted 1 day ago
Skill Required
Forward-Deployed-AI-EngineerApplied-AI-EngineerMachine-Learning-EngineerData-ScientistAI-EngineerForward-Deployed-ML-EngineerForward-Deployed-AI-EngineeringForward-Deployed-EngineerForward-Deployed-Software-EngineerAI EngineerAIGenerative AIMachine LearningScikit-learnStatisticsPrototypingEngineeringKubernetesanalyticalautomationSnowflakedesigningbuildingfintechetc.)Power BITestNGPythonPandasDockerdesignNumPyNLPRAGSQLFulltime
Key highlights
- Required experience: 3-4 years
- Location: India (Pune / Bangalore / Remote India)
- Key Benefit: No ML infrastructure tax (Everything runs natively in Snowflake)
- Notable Requirement: Strong Python and SQL fluency
Role overview
Bottomline is a global leader in business payments and cash management, seeking a Forward-Deployed AI Engineer (FDE) to join the Enterprise Data Services (EDS) AI Engineering function. In this role, you will turn business questions into working AI solutions that run in production, solving business problems natively in Snowflake from feature engineering through model training to deployment as a live service. The EDS team builds AI-native solutions where intelligence is the product, utilizing Snowflake's native AI capabilities (Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, ML Functions) to avoid external ML infrastructure or separate serving layers. You will focus on the science of AI solutions while collaborating with engineers who handle the platform infrastructure.
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
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: Snowflake experience (Snowpark Python, Cortex AI functions, ML model registry)
- Strong Preference: Experience with LLMs in production (prompt engineering, RAG architectures, evaluation of generative outputs)
- Strong Preference: Understanding of Cortex Agents, Cortex Search, or Cortex Analyst
- Strong Preference: Experience building evaluation sets and measuring AI quality systematically (not just accuracy, precision/recall tradeoffs, error analysis)
- Strong Preference: Familiarity with feature engineering for tabular business data (customer signals, transaction patterns, contract attributesetc)
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
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
Additional details
- 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
- Tech Stack: Snowflake (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)
- First 90 Days - Days 1-30: Learn Snowflake platform and Cortex AI capabilities; run through the AI project lifecycle on a small candidate
- First 90 Days - Days 30-60: Spike; own first AI candidate from prioritised backlog; run timeboxed spikes to prove positive signal; create first golden evaluation dataset; present findings to team
- First 90 Days - Days 60-90: Build; build spike to production standard with proper evaluation; pass through governance gates; ship first AI solution to production
- Bottomline is an equal opportunity employer committed to creating an inclusive and open environment for everyone
- Originally posted on Himalayas
