Enterprise Solutions Architect

The Role:

Solutions Architects at Fireworks are the technical and strategic owners of the customer relationship from the first discovery call through to production. You'll work with some of the most ambitious engineering teams in the world, translating complex business problems into concrete AI solutions built on the Fireworks platform.

This is a role that demands both technical depth and strong people skills. You'll need to earn the trust of ML engineers and VPs in the same meeting, scope and execute POCs without losing sight of the customer's definition of success, and know enough about inference, fine-tuning, and model architecture to make credible recommendations under pressure.

We hire SAs across two tracks. Both require strong technical grounding and sharp customer instincts; the difference is where each track places its emphasis.

Enterprise SA Track

• Works with digital native and large organizations — navigating multiple stakeholders, procurement cycles, and executive relationships
• Heavy emphasis on executive presence: equally comfortable presenting to a CTO and debugging a latency issue with an ML engineer
• Leads complex technical sales: discovery, solution design, POC execution, commercial negotiation
• Owns the account relationship end-to-end, including expansion and renewal
• Strong commercial instincts understands how to build a business case and close large deals

Applied AI Track

• Works with high-velocity accounts and technology partners startups, ISVs, and hyperscaler ecosystems
• Heavier emphasis on technical execution more time in the code, building integrations, running enablements
• Faster iteration cycles with less org navigation focused on shipping working solutions quickly
• Embeds with partner engineering teams to enable their AI practices and build joint solutions
• Comfortable operating across engineering, partnerships, and sales simultaneously

What You'll Work On:

Regardless of track, SAs at Fireworks own a consistent set of responsibilities:

Technical Discovery & Solution Design

• Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions
• Design end-to-end architectures for GenAI applications covering model selection, inference configuration, RAG design, and fine-tuning strategy

POC Scoping & Execution

• Define what a minimal, compelling proof-of-concept looks like and own it through to delivery. Prioritize and stack rank opportunities: manage scope creep, set realistic timelines, and keep the customer aligned on what success looks like
• Work alongside product and engineering teams to execute technically rigorous POCs

Performance Engineering

• Run inference sweeps and establish performance baselines for customer workloads
• Create and configure deployments tuned to specific latency, throughput, and cost targets

Fine-Tuning & Model Recommendations

• Guide customers on fine-tuning strategy and model recommendations: when to use SFT, DPO, or RFT, and which model family fits their use case
• Build and run fine-tuning pipelines directly for customers
• Evaluate model quality and help customers build robust eval pipelines

Account Ownership & Stakeholder Management

• Own the technical relationship across the account: from champion to executive sponsor
• Navigate complex organizations, build trust at multiple levels, and maintain momentum through long sales cycles
• Feed customer signal: deployment patterns, pain points, feature gaps — back into the product roadmap

What We're Looking For

• 5+ years in a technical, customer-facing role — Solutions Architect, Sales Engineer, Forward Deployed Engineer, Customer facing AI Engineer / Data Scientist or equivalent
• Hands-on experience with the LLM stack: inference trade-offs, fine-tuning methodologies (SFT, RFT, DPO), and deploying models at scale
• Strong Python skills: comfortable reading, writing, and debugging production code
• Exceptional communication: able to run a sharp discovery call, present to a VP, and explain reinforcement learning to an ML engineer in the same afternoon
• Experience with cloud infrastructure (AWS, Azure, GCP) and model serving at scale