Forward Deployment Engineer

Responsibilities

We are representing a confidential AI infrastructure client seeking a Forward Deployment Engineer to embed directly with enterprise customers and drive production deployments of generative AI solutions. In this role, you will:

• Design, build, and deploy production GenAI applications on specialized AI hardware and software platforms at strategic customer sites
• Architect and implement LLM-powered workflows including RAG pipelines, multi-agent systems, fine-tuning workflows, and custom coding solutions tailored to each customer's infrastructure and business requirements
• Optimize AI inference performance by benchmarking model throughput, latency, and accuracy against customer requirements and competitive baselines
• Troubleshoot and resolve production issues end-to-end across model, software, and hardware layers, serving as the primary technical escalation point in the field
• Translate customer needs into actionable product requirements and engineering feedback, representing field realities to Product and Engineering teams
• Collaborate with Account Executives and Solutions Engineers to shape technical sales strategy, scope engagements, and demonstrate platform capabilities during evaluations and proof-of-concepts
• Develop reusable accelerators, reference architectures, and internal playbooks that scale learnings across deployments
• Present technical findings, architecture decisions, and roadmap input at customer executive briefings and represent the organization at industry conferences

What We're Looking For

Required qualifications:

• 5+ years of hands-on engineering experience with a proven track record of shipping production AI/ML systems
• Deep expertise in GenAI application development: LLM orchestration, RAG, agentic frameworks (LangChain, LlamaIndex, DSPy), prompt engineering, and evaluation pipelines
• Strong foundations in ML fundamentals including model training, fine-tuning, inference optimization, quantization, and performance benchmarking
• Proficiency in Python (required); working knowledge of C++ or CUDA is a strong plus for hardware-layer debugging
• Experience deploying AI workloads on cloud infrastructure (AWS, Azure, GCP) with familiarity in containerization, orchestration (Kubernetes, Docker), and MLOps tooling
• Comfort engaging directly with customers: able to run technical discovery, set expectations, provide constructive feedback, and present to both executive and practitioner audiences
• Bachelor's or graduate degree in Computer Science, Electrical Engineering, Mathematics, Physics, or equivalent practical experience
• Willingness to travel up to 50% to customer sites based on engagement needs

Bonus qualifications:

• Experience with AI accelerators or custom silicon
• CUDA or low-level GPU programming skills
• Familiarity with VLLM or SGLang
• Enterprise AI deployments in regulated industries

How We Work with You

Candidates apply directly through Get A Job.ai. Our recruiting team will conduct an initial screening to understand your background and qualifications. After our review, we will submit qualified candidates to our client for consideration. Please do not contact the client directly—all communication and coordination will flow through our talent team at Get A Job.ai.

Pay

This position is based in the United States and offers competitive compensation. Specific pay details will be discussed with qualified candidates during the screening process.

Equal Employment Opportunity: Get A Job.ai is committed to fair and equitable recruiting practices. We evaluate all candidates based on qualifications and merit, and we work with clients who share our commitment to inclusive hiring.