AI Field Engineer, Singapore
Hybrid (Singapore)full-timePosted 2mo ago
About the role
AI Field Engineer role embedding with customers to turn complex AI problems into production systems, with hands-on delivery, customer-facing responsibilities, and executive-level collaboration. Located in Singapore, Singapore.
Responsibilities
- Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints.
- Architect inference foundations and size deployments for GenAI-driven core products to scale without infrastructure bottlenecks.
- Run load tests, establish latency/throughput/cost baselines, and tune deployments to targets.
- Deploy and validate new model families on inference frameworks (vLLM, SGLang), optimizing shapes, quantization, and serving patterns.
- Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology.
- Build and run fine-tuning pipelines with customers, balancing model families, compute cost, and quality targets.
- Design and implement evaluation frameworks measuring production-quality metrics.
- Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria.
- Own the technical relationship from first engagement through production deployment.
- Spend time on-site with customers, embedding with their teams.
- Identify recurring customer pain points and translate them into concrete product proposals.
- Codify repeatable deployment patterns and contribute to internal tooling, documentation, and platform.
- Provide customer signals to product roadmap with specificity and urgency.
Qualifications
- 5+ years in a hands-on, customer-facing technical role (e.g., Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder).
- Proven ability to ship production software with customers.
- 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 minimum; DPO/RFT a strong plus).
- Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.
- Exceptional communication skills for discovery calls, executive presentations, and latency debugging.
Skills
Required:PythonKubernetesLLM stackCloud platforms
Benefits
- meaningful equity
- competitive salary
- comprehensive benefits package
Last updated Jul 29, 2026. Interested? Apply on the company site.
About Fireworks AI
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