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Tiger Analytics

Delivers Data and AI transformation

Forward Deployed Engineer - Generative AI

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listed

About the Job

Requirements

  • Advanced knowledge of Vertex AI primitives, including Vertex AI Studio, Model Registry, endpoint deployment, Vertex AI Pipelines (Kubeflow), and Vertex AI Vector Search.
  • Hands-on experience with LLM orchestration tools such as LangChain, LlamaIndex, or AutoGen, and deep learning frameworks such as PyTorch or Hugging Face optimized for Google Cloud Platform infrastructure.
  • Production experience setting up, optimizing, and querying Vertex AI Vector Search or managed vector stores such as Milvus, Pinecone, and pgvector on Cloud SQL or Spanner.
  • Proficiency in model-serving frameworks such as vLLM, TGI, or Triton Inference Server deployed via Vertex AI or Google Kubernetes Engine, and robust automated model evaluation pipelines.
  • Deep expertise in Google Kubernetes Engine for managing GPU/TPU workloads, autoscaling, and scheduling.
  • Mastery of Terraform to provision secure, complex Google Cloud Platform environments, IAM roles, and Vertex AI resources.
  • Strong coding skills in Python or Go, with an emphasis on clean, concurrent code and use of the Google Cloud SDK.
  • Ability to manage customer expectations around LLM non-determinism, hallucinations, and performance trade-offs.
  • Willingness to travel to client sites to lead high-stakes, on-site deployment sprints.

Responsibilities

  • Deploy, fine-tune, and optimize large-scale Generative AI models and LLM orchestration frameworks within customer cloud environments.
  • Architect scalable infrastructure for AI workloads using GPU/TPU orchestration, high-performance storage, and low-latency networking.
  • Design and implement high-throughput data ingestion pipelines and vector database architectures for Retrieval-Augmented Generation.
  • Act as the primary technical consultant, guiding enterprise clients through AI safety, prompt engineering patterns, and inference cost optimization.
  • Feed edge-case deployment insights back to core AI research and platform engineering teams to improve product robustness.
  • Collaborate with cross-functional teams and business partners, help drive current and future strategy using analytical skills, and communicate business value and results.

Desired Qualifications

  • Python is preferred over Go.
  • Keep pace with advancements in the Generative AI landscape.
  • Isolate errors across complex software layers, from GPU drivers to prompt engineering logic.

About the Company

Tiger Analytics

Delivers Data and AI transformation

Tiger Analytics helps large organizations plan and execute Data and AI–led transformations at scale. It works with clients to translate business needs into data and AI initiatives, using an approach that combines business value mapping, agile operating models, and a platforms-based architecture, backed by partnerships with major hyperscalers. The company designs and delivers end-to-end data and AI solutions—from strategy and data engineering to model development and deployment—so that AI can be applied to tough, real-world problems. Unlike firms that rely mainly on standard playbooks, Tiger Analytics builds custom approaches and accelerators to suit each client’s value chain and operating model. With a global team of 5000+ technologists and consultants, the goal is to enable measurable value and practical outcomes through data and AI programs that work at enterprise scale.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
San Jose, CA
Founded
2010

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