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 ResponsibilitiesEnterprise 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 For3+ 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 HavesDirect 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.