Forward Deployed Engineer (Generative AI)

Tiger Analytics is looking for experienced Forward Deployed Engineer (Generative AI) with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

Role Overview

The Forward Deployed Engineer (FDE) drives the on-site deployment, integration, and scaling of our enterprise Generative AI solutions. This role embeds directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across multi-cloud environments (AWS, Azure, GCP). You will bridge the gap between AI research and production-grade cloud infrastructure.

You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

• AI Frameworks: Hands-on experience with LLM orchestration tools (LangChain, LlamaIndex, AutoGen) and deep learning frameworks (PyTorch, Hugging Face).
• Vector Databases: Production experience setting up and querying vector stores (Milvus, Pinecone, Qdrant, Chroma, or pgvector).
• Model Operations (LLMOps): Proficiency in model serving frameworks (vLLM, TGI, Triton Inference Server) and evaluation tools.
• Cloud & Containers: Advanced knowledge of cloud AI primitives (AWS Bedrock/SageMaker, Azure OpenAI, GCP Vertex AI) and Kubernetes (K8s) for GPU workloads.
• IaC & Automation: Mastery of Terraform or OpenTofu to provision complex multi-cloud compute environments.
• Programming: Strong coding skills in Python (preferred) or Go, with an emphasis on writing clean, concurrent code.

• →AI Solution Deployment: Deploy, fine-tune, and optimize large-scale Gen AI models and LLM orchestration frameworks within customer cloud environments.
• →Infrastructure Engineering: Architect scalable infrastructure for AI workloads utilizing GPU/TPU orchestration, high-performance storage, and low-latency networking.
• →Data & Retrieval Pipelines: Design and implement high-throughput data ingestion pipelines and Vector Database architectures for Retrieval-Augmented Generation (RAG).
• →Multi-Cloud Management: Build agnostic, resilient cloud deployments across AWS, Azure, and GCP using Infrastructure as Code (IaC).
• →Technical Advocacy: Act as the primary technical consultant, guiding enterprise clients through AI safety, prompt engineering patterns, and inference cost optimization.
• →Product Collaboration: Feed edge-case deployment insights back to core AI research and platform engineering teams to improve product robustness.

• AI Consultation: Ability to manage customer expectations around LLM non-determinism, hallucinations, and performance trade-offs.
• Rapid Adaptability: Passion for keeping pace with the weekly advancements in the Generative AI landscape.
• Critical Debugging: Exceptional skill in isolating errors across complex software layers, from GPU drivers up to prompt engineering logic.
• Mobility: Willingness to travel to client sites to lead high-stakes, on-site deployment sprints.