AI Field Engineer (Enterprise)

Fireworks AI; United States; On-site; USD 140,000 - 230,000; Full time

Job summary

Fireworks AI is seeking an experienced AI Field Engineer to embed with large customers and technology partners, turning complex GenAI problems into scalable production systems. You will balance hands-on coding with executive-level discussions on architecture, strategy, and business outcomes.

The role requires on-site collaboration, end-to-end POC execution, and deployment design while maintaining strong customer relationships and delivering production-grade solutions at enterprise scale.

Qualifications

• Strong Python production code skills.
• Hands-on with Kubernetes and cloud infra.
• Extensive experience deploying GenAI solutions in production.
• Proven ability to ship POCs to production at enterprise scale.
• Excellent communication with executives and engineers.

Responsibilities

• Build end-to-end GenAI deployments with customer teams.
• Run benchmarks, latency tests, and cost baselines at scale.
• Lead discovery calls and translate pain points into solutions.
• Architect deployments for enterprise-scale production environments.
• Collaborate with product and engineering to improve platforms.

Skills

Python

Kubernetes

Cloud Infrastructure

ML Engineering

Field Experience

Tools

vLLM

SGLang

TensorRT-LLM

Job description

• 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
• 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
• 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
• 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
• 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
• 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

Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founderDemonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else’s production environmentExceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoonExperience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructureWorking knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)10+ years in technical field or engineering rolesExperience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloadsExperience 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 systemsTrack record taking GenAI POCs from prototype to production-scale deploymentsExperience building or integrating agentic systems, tool-use chains, or AI-native developer toolchainsExperience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)