{"id":136780,"url":"https://alion.io/job/tiger-analytics-forward-deployed-engineer-generative-ai","title":"Forward Deployed Engineer (Generative AI)","company":{"id":3693,"name":"Tiger Analytics","domain":"tigeranalytics.com","url":"https://alion.io/company/tigeranalytics","size_band":"501-1000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":71,"open_postings":153,"ghost_share":0.49,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":47,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Solutions","role_family":"Solutions","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US","CA"],"hiring_countries_total":2,"salary":null,"salary_estimate":{"min_usd":109000,"max_usd":238000,"period":"year","method":"role_country_seniority_unknown","sample_n":4100},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AutoGen","optional":false},{"name":"GCP","optional":false},{"name":"Google AI Studio","optional":false},{"name":"Google Cloud Spanner","optional":false},{"name":"Google GKE","optional":false},{"name":"Hugging Face","optional":false},{"name":"IAM","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Milvus","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"TGI","optional":false},{"name":"TPU","optional":false},{"name":"Triton Inference Server","optional":false},{"name":"Vertex AI","optional":false},{"name":"vLLM","optional":false},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-08-25T00:00:00Z","employer_posted_date":"2026-08-25","last_verified_at":"2026-10-16T11:18:30Z","board_verified":true,"closed_at":null,"days_open":52,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":52},"description":"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.\nWe are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.\nRole Overview\nThe 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 Google Cloud Platform(GCP).. You will bridge the gap between AI research and production-grade cloud infrastructure.\nYou 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.\nRequirements\nTechnical Requirements\nGCP & Vertex AI Architecture: Advanced knowledge of Vertex AI primitives, including Vertex AI Studio, Model Registry, Endpoint deployment, Vertex AI Pipelines (Kubeflow), and Vertex AI Vector Search.\nAI Frameworks: Hands-on experience with LLM orchestration tools (LangChain, LlamaIndex, AutoGen) and deep learning frameworks (PyTorch, Hugging Face) optimized for GCP infrastructure.\nVector Databases: Production experience setting up, optimizing, and querying Vertex AI Vector Search, or managed vector stores like Milvus, Pinecone, and pgvector (Cloud SQL/Spanner).\n Model Operations (LLMOps): Proficiency in model serving frameworks (vLLM, TGI, Triton Inference Server) deployed via Vertex AI or GKE, alongside robust automated model evaluation pipelines.\nContainers & Kubernetes: Deep expertise in Google Kubernetes Engine (GKE) for managing GPU/TPU workloads, autoscaling, and scheduling.\nIaC & Automation: Mastery of Terraform to provision secure, complex GCP environments, IAM roles, and Vertex AI resources.\nProgramming: Strong coding skills in Python (preferred) or Go, with an emphasis on writing clean, concurrent code and utilizing the Google Cloud SDK.\nKey Responsibilities-\nAI Solution Deployment: Deploy, fine-tune, and optimize large-scale Gen AI models and LLM orchestration frameworks within customer cloud environments.\nInfrastructure Engineering: Architect scalable infrastructure for AI workloads utilizing GPU/TPU orchestration, high-performance storage, and low-latency networking.\n Data & Retrieval Pipelines: Design and implement high-throughput data ingestion pipelines and Vector Database architectures for Retrieval-Augmented Generation (RAG).\nTechnical Advocacy: Act as the primary technical consultant, guiding enterprise clients through AI safety, prompt engineering patterns, and inference cost optimization.\nProduct Collaboration: Feed edge-case deployment insights back to core AI research and platform engineering teams to improve product robustness.\nSoft Skills-\nAI Consultation: Ability to manage customer expectations around LLM non-determinism, hallucinations, and performance trade-offs.\nRapid Adaptability: Passion for keeping pace with the weekly advancements in the Generative AI landscape.\nCritical Debugging: Exceptional skill in isolating errors across complex software layers, from GPU drivers up to prompt engineering logic.\nMobility: Willingness to travel to client sites to lead high-stakes, on-site deployment sprints.\nBenefits\nThis position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment 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