Forward Deployed Engineer

Newell Brands•Hoboken, NJ

About The Position

The Forward Deployed Engineer designs and builds AI-enabled capabilities for marketing, eCommerce, content and related business workflows. The role works directly with teams responsible for content, product data, creative, syndication and other workflows to understand their needs and develop practical solutions. It is a hands-on, high-visibility position focused on technical delivery, with field learnings informing the product vision and roadmap alongside the Head of Digital Product & Innovation, eCommerce. Solutions developed in the field may be turned into reusable, productized capabilities and deployed across the business. The engineer prototypes, builds, deploys and operates AI agents, workflow automations, integrations, data pipelines, machine-learning models and lightweight applications. The role covers the full solution lifecycle, including architecture, production deployment, evaluation, monitoring, documentation and user training. Work includes integrating AI and content capabilities with Azure and enterprise platforms such as Adobe and Salsify, while meeting enterprise security, access-control and data-governance requirements.

Requirements

Bachelor’s degree in computer science, engineering, data science or a related field, or equivalent practical experience.
Five or more years of experience in software engineering, data engineering, solutions engineering or a similar role, including forward-deployed engineering or consulting experience, with technical delivery for customers or business teams.
Strong Python and SQL skills.
Software engineering fundamentals, including version control, code review and automated testing.
Experience with Azure and DevOps practices, including CI/CD, infrastructure as code and cloud deployment.
Data science experience, including feature engineering, model selection, validation and statistical evaluation, and moving models from experimentation to production.
Hands-on machine-learning experience with information extraction and classification, such as named-entity recognition, multi-label and hierarchical classification, embeddings, and fine-tuning transformer or large language models.
Experience building with large language models, agent frameworks and orchestration, prompt and context design, evaluation and monitoring.
Experience with classification, taxonomy mapping or entity-resolution models.
Working knowledge of current AI tools and methods, demonstrated through examples of work built or tested.

Responsibilities

Map workflows with New Item Setup, Marketing, Brand, eCommerce, Content, Media, Commercial and IT teams to identify data issues, handoffs and rework.
Take on emerging marketing and eCommerce needs, help prioritize them alongside ongoing work, and identify the underlying problem behind requests.
Translate business requirements into scoped technical solutions with clear specifications and success metrics.
Contribute to solution architecture, including model, orchestration and data-design choices, in partnership with enterprise IT; recommend alternative approaches when appropriate.
Build and own technical components through design, deployment, testing and production monitoring.
Prototype and deploy AI agents, workflow automations, integrations, data pipelines and lightweight applications, and take suitable prototypes into production in accordance with enterprise standards.
Build AI agents and workflows that act in business systems, including prompts, tool calls, retrieval and context layers.
Develop classification and enrichment models to map, validate and generate product attributes and categories against company taxonomy and retailer requirements.
Train, tune and deploy machine-learning models that extract and classify information from text, images and documents at scale.
Select appropriate techniques for each use case, from rules and classical models to fine-tuned and LLM-based extraction, and design feedback loops that use human corrections to retrain models.
Connect copy and generative-AI content capabilities into workflows using custom Azure solutions and enterprise tools such as Adobe and Salsify.
Build data pipelines and integrations across Azure, enterprise data platforms, PIM, syndication and digital asset platforms, including Stibo, Salsify and Adobe.
Partner with enterprise IT to meet security, access-control and data-governance requirements and integrate with legacy systems.
Use Claude Code and similar AI-assisted development tools to build and test more quickly.
Test AI outputs using defined test sets, quality metrics and human review.
Measure extraction and classification performance using precision, recall and F1 on labeled datasets; track confidence thresholds, error patterns and model drift.
Monitor deployed solutions, diagnose root causes of issues and fix them.
Build dashboards reporting solution health and business results, including cycle time, data quality, cost avoidance and content readiness.
Help measure and report impact in terms such as time saved, cost avoided, revenue influenced and quality improved.
Document solutions, train business teams to use and extend them independently, gather feedback and iterate until solutions are incorporated into team workflows.
Contribute to product vision and roadmap by combining field learnings with the leader’s strategy and priorities.
Turn recurring field needs into reusable capabilities and help determine what to build once, configure or not build.
Bring data, working demonstrations and technical recommendations to stakeholder discussions to support decisions.
Communicate progress, tradeoffs and risks clearly to working teams and senior leaders.
Stay current on AI tools and methods and recommend when to adopt, build or retire capabilities.