What does a Forward Deployed Engineer do at Eliza?

Forward Deployed Engineers at Eliza are embedded directly with enterprise customers to implement and customize the platform, build integrations, and solve technical problems in real-world client environments. They combine software engineering skills with direct customer interaction, writing production code while also serving as the primary technical point of contact.

What skills are required for this Forward Deployed Engineer role?

The Forward Deployed Engineer role at Eliza typically requires strong software engineering skills (Python, TypeScript, SQL), experience with APIs and system integration, comfort with cloud platforms, and the ability to communicate complex technical topics to non-technical stakeholders. Most FDE roles require 3-7 years of engineering experience.

Is this Forward Deployed Engineer position at Eliza remote?

Remote availability varies by Eliza FDE role. Some positions are fully remote, others require travel to customer sites, and some are hybrid. Check the full job description above for the specific location requirements for this posting.

Eliza

Forward Deployed Engineer

Remote Posted April 26, 2026

Apply on Indeed All FDE Jobs

Role Description

About Us

We are a technology services company dedicated to helping organizations build and deploy cutting-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real-world AI applications to life. Our projects combine deep technical expertise with hands-on client collaboration to solve high-impact problems.

Role Overview

We are seeking a Forward Deployed Engineer (FDE) to work closely with our clients, translating complex business needs into scalable, production-ready AI solutions. As an FDE, you will serve as the technical face of our company on the ground—embedding with client teams, shaping solution architectures, and ensuring successful delivery. This role is perfect for engineers who love solving real-world problems, working directly with customers, and navigating the intersection of consulting and engineering.

Key Responsibilities

1. Client-Facing Solution Delivery

Partner directly with client stakeholders to understand requirements, constraints, and business objectives.
Lead the technical design and hands-on implementation of custom AI systems—including model integration, data pipelines, APIs, and deployment infrastructure.
Rapidly prototype and iterate with clients in live environments.

2. Full-Stack AI Engineering

Build and deploy ML/AI solutions using technologies like Python, TensorFlow/PyTorch, LangChain, and cloud-native tools.
Integrate LLMs and other generative models into client products and workflows.
Support model fine-tuning, prompt engineering, and evaluation pipelines where applicable.

3. Cross-Functional Collaboration

Work with internal teams (product, design, research) to shape reusable components and frameworks based on deployment experiences.
Contribute client feedback and frontline insights to improve service delivery and product strategy.

4. Technical Advisory & Enablement

Advise client technical teams on best practices for AI/ML development and deployment.
Deliver hands-on workshops, documentation, and training to enable long-term client success.
Guide clients through infrastructure and architecture decisions (e.g., cloud, security, scalability).

Qualifications

Required

2+ years of software engineering experience, ideally in full-stack or backend-focused roles.
Hands-on experience delivering real-world ML/AI projects, either independently or in collaboration with data science teams.
Strong programming skills (Python required; familiarity with JavaScript/TypeScript, Go, or similar a plus).
Comfort with modern cloud platforms (AWS, GCP, or Azure) and CI/CD workflows.
Excellent communication and client interaction skills.

Preferred

Experience with LLMs (e.g., OpenAI, Anthropic, Cohere), vector search, or prompt engineering.
Prior consulting, professional services, or customer-facing technical roles.
Familiarity with MLOps practices and tools (e.g., MLflow, Weights & Biases, SageMaker).
Knowledge of common enterprise security, data privacy, and compliance constraints.

What We Offer

Competitive compensation (salary + deployment bonuses or client uplift incentives).
Equity options in a growing AI services company.
Flexibility to work across industries and problem domains.
A collaborative, mission-driven team passionate about the real-world impact of AI.

About Forward Deployed Engineering

Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.

Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.

Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.