Enterprise AI Field Engineer

AI Engineer

Job Type: Full-time

About the Opportunity

Zaddy Solutions is partnering with a high-growth, late-stage startup AI infrastructure company to hire multiple Enterprise AI Engineers.

This company is well-capitalized, scaling quickly, and backed by strong conviction from top-tier investors. At roughly 200 people, they are large enough to have major enterprise traction, but still early enough for high-impact ownership and meaningful equity.

This is a customer-facing, hands-on engineering role for someone who can help enterprise customers move from ambitious GenAI concepts to production systems. The right person will sit at the intersection of generative AI engineering, infrastructure, enterprise deployment, and technical relationship ownership.

This is not a slideware role. This is a role for someone who has built, evaluated, deployed, and supported real AI/ML systems inside customer environments.

The Role:

On behalf of our client, we are seeking AI Field Engineers who can lead technical discovery, build production-ready GenAI solutions, and serve as trusted technical partners to sophisticated enterprise customers.

The mission: help enterprise customers evaluate, design, deploy, and scale GenAI systems using modern model architectures, open-model infrastructure, fine-tuning workflows, and production-grade deployment patterns.

The successful candidate will be equally comfortable writing Python, working through GPU and cloud infrastructure decisions, leading a customer POC, advising on model strategy, and explaining trade-offs to senior technical and business stakeholders.

Key Responsibilities

• Enterprise Technical Discovery: Lead technical conversations with enterprise customers to understand use cases, infrastructure requirements, security constraints, and deployment needs.
• POC Development & Production Integration: Build end-to-end POCs and production integrations directly inside customer environments, working through infrastructure, compliance, and operational constraints.
• Model Architecture & Evaluation: Run load tests, evaluations, and technical assessments to validate model architectures, serving approaches, and deployment setups.
• Fine-Tuning & Model Strategy: Advise customers on model selection and fine-tuning strategies, including workflows such as SFT, DPO, RFT, and related approaches.
• Technical Relationship Ownership: Own the technical relationship across complex accounts, identify champions, address detractors, and align engineering, product, security, and executive stakeholders.
• Field-to-Product Feedback Loop: Capture recurring customer patterns, implementation challenges, and product gaps, then feed those insights back into the product and engineering roadmap.

Technical Requirements

• AI/ML Field Experience: 3+ years in customer-facing AI, ML, infrastructure, solutions architecture, applied AI, or field engineering roles.
• Production AI/ML Delivery: Demonstrated experience shipping real AI or ML production code into customer environments, not only advisory work, demos, or research prototypes.
• LLM Infrastructure Experience: Hands-on experience with LLM inference and/or training using open-model frameworks, modern serving stacks, and fine-tuning workflows.
• Fine-Tuning Exposure: Practical experience with SFT is important. Exposure to DPO, RFT, or similar advanced fine-tuning approaches is a strong plus.
• Python & Infrastructure: Strong Python skills, plus comfort with GPUs, cloud infrastructure, and production deployment environments.
• Cloud & Orchestration: Experience with AWS, Azure, or GCP, plus container/orchestration tools such as Kubernetes.

Who You Are

• A Hands-On AI Builder: You have built and deployed real AI/ML systems, and you are comfortable getting into the code, infrastructure, and implementation details.
• Customer-Facing Technical Partner: You can lead discovery, scope a POC, explain technical trade-offs, and build trust with enterprise stakeholders.
• Enterprise-Ready Communicator: You can go deep with an engineer and translate the same concept clearly for senior leadership.

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• Production-Minded: You understand that enterprise AI work is not just about model performance. It also requires security, compliance, scalability, reliability, and operational fit.
• Comfortable in High-Velocity Environments: You can operate in a scaling company where the field, product, and engineering teams are learning quickly from customer needs.

This Is Not the Right Fit If

• Your LLM experience is primarily limited to closed-model APIs, wrapper libraries, or app-layer experimentation without meaningful exposure to open-model inference or fine-tuning.
• Your background is mostly advisory, strategy, or research-focused without evidence of shipping production systems.
• Your experience has been exclusively in large, highly structured environments with limited exposure to startups, field engineering, or fast-moving customer-facing technical work.

Location, Travel & Visa Support

This is a US-based, remote-friendly role, with the option to work from coastal hubs if desired.

Regular domestic travel is expected for customer discovery, POCs, and production rollout support.

• Visa support may be available for select categories, including common transfer paths for experienced engineers.