Forward Deployed Engineer
Posted 3 days ago
The role in short
Embed with enterprise customers to design, build, and deploy AI-driven solutions and custom integrations using Optimizely APIs and RAG workflows.
Company Description
We're not here to add to the noise. We're here to cut through it- with AI that actually works for marketers.
From AI-powered content creation to world-class CMS and the industry's most trusted experimentation platform, Optimizely is the tool modern marketers actually want to use. AI-Ready. Set. Go.
10,000+ brands including H&M, PayPal, and Zoom already get it. So do Gartner, Forrester, and IDC, who consistently recognize us as leaders in MarTech.
But here's the thing about building great products: it takes great people. Our 1,600+ Optimizers across 12 global offices are curious, collaborative, and refreshingly human. We don't do corporate speak. We do real conversations, big ideas, and genuinely care for the work we make together.
If you want to be part of a team that's shaping the future of marketing technology — and actually enjoys doing it — you're in the right place.
Find us on Instagram: @optimizely
Job Description
The customer-focused Forward Deployed Engineer will be the technical bridge between Optimizely's innovative AI products and our enterprise customers. They will leverage deep technical expertise and communication skills to design, build, and deploy AI-driven solutions that seamlessly integrate with Optimizely's platforms, enabling customers to harness the power of AI.
Job Responsibilities
- Customer Consultation & Solution Design: Collaborate with customers to understand AI use cases, data needs, and integrations, assisting in translating objectives into technical plans leveraging Optimizely AI (Opal).
- Lead Technical Deployments: Participate in the configuration and deployment of Optimizely AI (Opal), interacting with Optimizely products and customer tech stacks via public APIs.
- Develop Custom Tools & Integrations: Support in the design and build of custom applications or connectors for AI workflows, especially Retrieval Augmented Generation (RAG), using Optimizely APIs and pre-trained AI models.
- Technical Troubleshooting & Optimization: Perform troubleshooting, debugging, and performance optimization for deployed AI solutions to ensure stability and efficiency.
- Technical Training & Enablement: Support workshops and knowledge transfer for customer teams to effectively use, manage, and extend Optimizely AI (Opal).
- Cross-Functional Collaboration: Collaborate effectively with Optimizely's Product, Engineering, and Customer Success teams to provide feedback and ensure alignment.
- Best Practices & Advocacy: Apply AI deployment best practices, understanding customer needs and aligning with Optimizely's product capabilities.
Qualifications
- 3+ years in a customer-facing technical role (e.g., Solutions Architect, Implementation Engineer, FDE) with a focus on software deployment and integration.
- Retrieval Augmented Generation (RAG): Familiarity with RAG concepts and experience with building tools/applications for RAG workflows (e.g., interacting with vector databases, embedding services, prompt engineering).
- Prompting Proficiency: Good understanding of prompting principles and best practices for optimizing AI model output.
- Programming Proficiency: Programming skills in JavaScript, TypeScript, Python for building integration layers, applications, and automation scripts via APIs.
- API & System Interaction: Proficiency in interacting with external systems via public APIs, including CRUD operations.
- Business Analysis: Experience in analyzing business requirements and translating them into technical solutions.
- Problem-Solving: Strong analytical and problem-solving skills for diagnosing and resolving complex technical issues.
- Communication: Strong verbal and written communication skills to articulate complex technical concepts to diverse audiences.
- Customer Focus: Customer-centric mindset, with ability to build rapport and contribute to customer success.
- Adaptability: Ability to quickly adapt to new technologies, product updates, and customer environments
Preferred Skills:
- Data Integration: Proficiency in data integration, transformation (ETL), and data governance techniques for AI solution consumption.
- Optimizely Product Knowledge: Experience with Optimizely products (CMS, CMP, Web Experimentation, Feature Experimentation, ODP).
- AI Deployment Expertise: Familiarity with AI solution deployment and integration in enterprise environments, including understanding data flow and system integration.
- Consulting Experience: Exposure to customer-facing engineering, solutions consulting, or technical pre-sales roles.
Education
- Bachelor's or Master's degree in Computer Science, Engineering, or related technical field.
Additional Information
All your information will be kept confidential according to EEO guidelines.
Optimizely is committed to a diverse and inclusive workplace. Optimizely is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Prep for this interview
3 questions likely to come up at Optimizely, each matched to a line in this posting.
- 1Integrations and APIs
They wrote: “Programming Proficiency: Programming skills in JavaScript, TypeScript, Python for building integration layers, applications, and automation scripts via APIs.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 2LLMs, RAG and agents
They wrote: “Retrieval Augmented Generation (RAG): Familiarity with RAG concepts and experience with building tools/applications for RAG workflows (e.g., interacting with vector databases, embedding services, prompt engineering).”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 3Coding
They wrote: “From AI-powered content creation to world-class CMS and the industry's most trusted experimentation platform, Optimizely is the tool modern marketers actually want to use. AI-Ready. Set. Go.”
Build a small in-memory cache. Then add expiry. Then a size limit.
How to answer
Why they ask: Staged exercises show how your code holds up when requirements change mid-interview, like at a customer.
- Start with get and set on a dictionary, plus tests
- Store an expiry time with each value and check it on read
- Track use order and evict the least recently used entry when full
- Say what you'd change for many threads or many machines
Tip: Keep the first version simple and well tested so the next stage is a small change.
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