AI Forward Deployed Engineer

We are seeking a high-caliber AI Forward Deployed Engineer (AI FDE) to bridge the gap between our proprietary foundation models and real-world enterprise adoption. In this role, you will embed directly with our strategic enterprise clients to build production-grade, business-critical applications powered by our state-of-the-art native models.

You will act as an applied AI specialist and production engineer. You will not just integrate APIs-you will customize model behavior, optimize inference architectures, design complex RAG and agentic workflows, and build high-throughput data pipelines directly inside client environments. You will also serve as the primary feedback pipeline to our core Research and Foundation Model teams, translating real-world operational challenges into future model capabilities.

Key Responsibilities Enterprise AI Integration: Embed alongside client engineering and product teams to design, build, and deploy custom applications built on our foundation models.

Applied Model Optimization: Tailor model output for client-specific domains using techniques like post-training, fine-tuning, parameter-efficient adaptations (LoRA/QLoRA), custom prompt engineering, and guardrails.

Architecture & Orchestration: Build production-grade AI systems, including multi-step agentic workflows, complex Retrieval-Augmented Generation (RAG) pipelines, dynamic context routing, and hybrid vector search architectures.

Production-Grade Engineering: Write clean, scalable code for high-performance inference endpoints, real-time data ingestion pipelines, and client infrastructure integrations (AWS, GCP, Azure, on-prem, air-gapped).

Research-to-Field Feedback Loop: Interface directly with our internal Foundation Model Research team to relay edge-case failures, enterprise data constraints, and model performance gaps to directly inform our next-generation model training runs.

Technical Leadership: Lead technical discovery sessions, scope production milestones, and advocate for AI safety, security, and compliance standards (evals, privacy, data governance) within enterprise client teams.

What We're Looking For 3+ years of experience in software engineering, backend systems, or machine learning engineering, with strong experience building and deploying generative AI systems.

Strong AI/ML Fundamentals: Deep understanding of Transformer architectures, context windows, tokenization, embeddings, vector databases, and evaluation frameworks (e.g., Ragas, DeepEval).

Production Coding Mastery: Proficiency in Python, Rust, Go, or C++, alongside standard ML frameworks (PyTorch, Hugging Face, vLLM, LangChain, LlamaIndex).

Data & Cloud Infrastructure: Experience with large-scale data processing (Spark, Ray, SQL), cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and GPU-accelerated inference deployment.

Customer-Facing Ownership: Ability to communicate complex model mechanics, latency trade-offs, and architectural decisions clearly to both client CTOs and hands-on developers.

Nice to Haves Direct experience deploying open-weights or proprietary foundation models in enterprise environments.

Background in domain-specific AI applications (e.g., legal tech, biomedical data, automated coding, quantitative finance).

Expertise in fine-tuning, RLHF/DPO, or synthetic data generation pipelines.

Background in high-stakes field engineering roles at leading AI labs, platforms, or technical consultancies.