Note: The job is a remote job and is open to candidates in USA. Tiger Analytics is looking for experienced Forward Deployed Engineer (Generative AI) with Gen AI experience to join our fast-growing advanced analytics consulting firm. The role involves deploying, integrating, and scaling enterprise Generative AI solutions while collaborating with customer engineering teams to operationalize Large Language Models across multi-cloud environments.
Responsibilities
- Deploy, fine-tune, and optimize large-scale Gen AI models and LLM orchestration frameworks within customer cloud environments
- Architect scalable infrastructure for AI workloads utilizing 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 (RAG)
- Build agnostic, resilient cloud deployments across AWS, Azure, and GCP using Infrastructure as Code (IaC)
- 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
Skills
- Hands-on experience with LLM orchestration tools (LangChain, LlamaIndex, AutoGen) and deep learning frameworks (PyTorch, Hugging Face)
- Production experience setting up and querying vector stores (Milvus, Pinecone, Qdrant, Chroma, or pgvector)
- Proficiency in model serving frameworks (vLLM, TGI, Triton Inference Server) and evaluation tools
- Advanced knowledge of cloud AI primitives (AWS Bedrock/SageMaker, Azure OpenAI, GCP Vertex AI) and Kubernetes (K8s) for GPU workloads
- Mastery of Terraform or OpenTofu to provision complex multi-cloud compute environments
- Ability to manage customer expectations around LLM non-determinism, hallucinations, and performance trade-offs
- Passion for keeping pace with the weekly advancements in the Generative AI landscape
- Exceptional skill in isolating errors across complex software layers, from GPU drivers up to prompt engineering logic
- Willingness to travel to client sites to lead high-stakes, on-site deployment sprints
- Strong coding skills in Python (preferred) or Go, with an emphasis on writing clean, concurrent code
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