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Added 10 mins ago
H
Senior Fire Protection Engineer
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- Full-time
…built environment. This isn’t just a job, it’s a chance to lead innovation, engineer impact, and build a legacy of…
Added 25 mins ago
FT
AI Field Engineer, EMEA
FDE Times
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- Full-time
…production issues, and architect deployments. But you also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements that compress the…
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FDE Times

AI Field Engineer, EMEA

London, ON, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Nov 22, 2026
Posting language: English
Posted: Nov 7, 2026

Original job description

Job Description

About Us

Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.

In The Last Few Months Alone We Launched Fireworks Training, Partnered With Microsoft Azure Foundry, And Published Research Straight From Our Production Systems. A Few Examples Of What That Looks Like In Practice

Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog)
Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog)
The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog)

The Role

AI Field Engineers at Fireworks are the technical tip of the spear. You embed with our most ambitious customers and technology partners to turn complex AI problems into production systems, fast. The role sits at the intersection of engineering, product, and customer delivery. You are hands-on-keyboard building POCs, MVPs, and production integrations, while also holding your own in executive-level conversations about architecture, strategy, and business outcomes.

You spend most of your time building. You ship code, run benchmarks, debug production issues, and architect deployments. But you also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements that compress the feedback loop from field to roadmap. This is a role for engineers who are comfortable on-site with customers, building the relationships and trust that happen in person, not just over a call.

The Segment

As a Field Engineer in the Enterprise track you will work with large organizations and digital-native companies adopting GenAI across the business. These engagements span more stakeholders and longer cycles, so you will manage executive relationships and align teams while staying hands-on in the code. The emphasis is on pairing strong technical delivery with the executive presence to earn trust across an org: discovery, solution design, POC execution, and the path to production at enterprise scale.

What You'll Work On

Technical Delivery and Deployment

Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints.
For customers whose core product is built on GenAI, architect the inference foundations that capability depends on, and size deployments so they can scale in their market without infrastructure becoming the bottleneck.
Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets
Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads.

Model Strategy and Fine-Tuning

Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology.
Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets.
Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores.

Customer Engagement and Stakeholder Management

Many of our customers exist because of GenAI. Help them bake frontier model capabilities into their core offering and turn that into a durable competitive edge.
Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions.
Own the technical relationship from first engagement through production deployment. Earn trust with ML engineers and VPs in the same meeting.
Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens.

Product Feedback and Platform Improvement

Identify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features.
Codify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself.
Feed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency.

What We're Looking For

Minimum Qualifications

5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder.
Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment.
Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering.
Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus).
Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.
Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon.

Preferred Qualifications

10+ years in technical field or engineering roles.
Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads.
Experience operating as a technical authority inside a customer's environment building within their infrastructure, navigating their constraints, and shipping code that runs in their production systems.
Track record taking GenAI POCs from prototype to production-scale deployments.
Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex).
Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.

Why Fireworks?

Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI - no bureaucracy, just results.
Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.

Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.

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More about this job

Responsibilities

Build and deploy customer-facing AI solutions, including POCs, production integrations, inference infrastructure, fine-tuning pipelines, and evaluation frameworks. Lead customer discovery and technical relationships, work on-site with customer teams, and turn recurring deployment challenges into product improvements and reusable tooling.

Requirements

Requires at least five years in a hands-on, customer-facing technical role, strong Python skills, experience shipping production software in customer environments, and working knowledge of Kubernetes, cloud and GPU infrastructure, and the LLM stack. Strong communication skills are essential; experience with inference frameworks, hyperscaler AI platforms, and taking GenAI prototypes to production is preferred.

Skills

Python
Kubernetes
Infrastructure Engineering
LLM Inference
Model Serving
Fine-Tuning
Cloud Infrastructure
GPU Deployment
Load Testing
Performance Optimization
Customer Discovery
Stakeholder Management
Evaluation Frameworks
Technical Communication
Agentic Systems

Visa sponsorship

Not detected in the job text

Keywords

Python
Kubernetes
AWS
Azure
GCP
GPU Infrastructure
LLM Inference
Model Serving
vLLM
SGLang
TensorRT-LLM
Fine-Tuning
Supervised Fine-Tuning
DPO
RFT
Load Testing
Latency
Throughput
Quantization
Evaluation Frameworks
GenAI
Agentic Systems
Azure AI Foundry
AWS Bedrock
Amazon SageMaker
Google Vertex AI
Production Deployment
Proofs Of Concept
Inference Frameworks
Technical Discovery

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