Forward Deployed Engineer

Role Overview

We are looking for a Forward Deployed Engineer (FDE) who thrives in the customer's environment, not just our own. You will work directly inside our customers' systems, including their marketing cloud, to make those environments more AI-enabled and AI-native. This is a hands-on engineering role with a high degree of customer contact: you will write AI agents and ship them into production alongside the people who depend on them.

A typical engagement places you in a customer-facing pod alongside a Solutions Engineer and a Customer Success representative. The pod's goal is to design, stand up, and operationalize an AI-powered system that delivers a concrete customer outcome. An early focus is next-best-action decisioning for lifecycle marketing programs, but the underlying capability is broader: building production AI agents that solve whatever high-value problem a given customer has, and the primary use case may pivot as the market dictates.

The systems you build use AI agents to ingest customer signals, reason over customer context, recommend or take the next action, integrate with the customer's existing workflows, and support measurement and continuous learning. You are responsible for turning a reference architecture into a working system that fits the realities of each customer's data, tooling, and constraints.

Localytics takes an API-first and MCP-first approach: capabilities are exposed as well-designed APIs and MCP-compatible tools so that both Localytics agents and customers can compose them. This role partners closely with Engineering, the Head of Product, and the CTO, feeding field learnings back into the core platform and agent tooling.

About Localytics

Localytics is an AI-enablement firm for enterprise marketing organizations. We build, integrate, and operate marketing AI for modernizing teams.

We have been working inside CRM marketing for fifteen years. We have a long history of successfully supporting enterprise brands through our mobile analytics and engagement platform. Today, our mission is to help modernizing brands integrate the latest technologies across their marketing workflow, help them operate it, dramatically impacting outcomes.

Localytics is a lean, fast-growing team at an exciting juncture. We are building a work culture that values integrity, ownership, and impact. No matter your role, you'll have real responsibility, influence, and the opportunity to build things that matter.

What You'll Do

Embed in the Customer's Environment

Work inside someone else's environment, codebase, and constraints, earning trust quickly and operating with judgment when the customer's reality does not match the reference architecture
Stand up the data platform and supporting infrastructure inside the customer's own cloud, wiring pipelines, storage, and services into their account rather than depending on a central team to provision it
Treat every customer environment as different, reaching for the simplest integration that works and avoiding over-engineering for a hypothetical future state

Ship Outcomes as Part of a Pod

Measure success by the customer outcome shipped, not the elegance of the solution, and do the unglamorous integration work needed to get a system into production
Work as one part of a customer-facing pod alongside a Solutions Engineer and Customer Success, sharing ownership of the engagement

Communicate Across Audiences

Translate between technical implementation and business outcomes, keeping both the customer and the internal team aligned throughout the engagement

Capabilities You Bring

Engineering Foundations

Hands-on coding: strong general-purpose engineering skills with a primary language such as Python, Go, or Java. Comfortable writing production code, not just prototypes
Systems integration: proven experience integrating disparate systems via APIs, webhooks, event streams, and data pipelines, often against incomplete or messy documentation
API and MCP design: designs clean, well-documented APIs and exposes capabilities as MCP-compatible tools so that agents, both Localytics's and the customer's, can consume them. Treats API-first and MCP-first design as the default, not an afterthought
Cloud: hands-on experience deploying and operating services in a major cloud (AWS preferred), including the basics of compute, storage, networking, and IAM
Data platform engineering: comfortable working with customer data at scale through at least one of data lakes (S3, Parquet, Delta Lake, Apache Iceberg), distributed processing with Spark, or data warehouses (Snowflake, Athena). Can move data from a customer's source systems into a form the agents can reason over

AI Agents & Decisioning

Applied use cases: experience taking an AI system from concept to a concrete outcome, ingesting signals, assembling context, reasoning over options, and triggering downstream actions
Agent architecture: understands the building blocks of agentic systems (tool use, retrieval, multi-step reasoning, state management) and can choose or build the right orchestration approach rather than reaching for a framework by default. Familiarity with frameworks such as LangGraph, LangChain, or CrewAI is useful but secondary to understanding the primitives
Production reliability: experience making LLM systems reliable in production, including structured output validation (e.g. Pydantic), guardrails on agent outputs, retry and fallback handling for flaky model calls, and managing token cost and latency at scale

Customer-Facing Delivery

Engagement delivery: experience delivering technical work directly with customers, whether in consulting, forward-deployed, solutions engineering, or professional services contexts
Communication: able to translate between technical implementation and business outcomes, and to keep both the customer and the internal team aligned

Nice to Have

Experience as a Forward Deployed Engineer, solutions architect, or implementation consultant at an enterprise software or AI company
Marketing cloud: familiarity with at least one enterprise marketing cloud or lifecycle marketing platform (Braze, Iterable, Salesforce Marketing Cloud, Adobe, or similar) and how campaigns, journeys, and audiences are configured
Customer data: comfortable working with customer behavioral and event data, including ingestion, transformation, and the privacy considerations that come with it
Experience with measurement and experimentation: A/B testing, holdouts, and attribution for marketing programs

Outcomes You Will Deliver

Production AI in Customer Systems

Production AI agents live in customer environments: design, stand up, and operationalize AI agents that run in production inside customer systems, moving engagements from pilot to production rather than leaving behind a prototype
AI-native workflows: integrate AI agents into the customer's marketing cloud and surrounding tooling so that recommendations and actions flow into real workflows, not a side dashboard
Signal-to-action pipelines: build the pipelines that ingest customer signals, assemble customer context, and feed the agents that reason over that context and recommend or take action

Measurable, Repeatable Impact

Measurement and continuous learning: instrument each deployment so the customer can measure impact and so the system improves over time through feedback loops rather than one-time tuning
Time to value: compress the time between engagement kickoff and demonstrable customer outcome, establishing a repeatable playbook the pod can reuse across customers
Field-to-platform feedback: channel patterns, gaps, and reusable components discovered in the field back into the core Localytics platform and agent tooling, so each engagement makes the next one faster