Sprinklr

Forward-Deployed AI Engineer

Sprinklr

full-time

Posted on:

Origin:  • 🇺🇸 United States • California, Massachusetts, New York

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Salary

💰 $161,000 - $269,000 per year

Job Level

Mid-LevelSenior

Tech Stack

AirflowCloudPythonRay

About the role

  • Build and deploy LLM-powered solutions (agents, RAG pipelines, tools integration) directly with enterprise customers.
  • Collaborate with cross-functional teams including Product Managers, Solutions Architects, and Customer Success to understand client requirements and deliver tailored AI solutions.
  • Iterate quickly on customer-facing use cases, incorporating feedback loops to improve model and system performance.
  • Support customers in implementation, fine-tuning, and integration of LLMs in production environments.
  • Share insights from deployments with core engineering and product teams to influence roadmap and feature prioritization.
  • Serve as a technical point of contact during deployments and early adoption phases and capture feedback from real-world usage.
  • Occasionally travel to customer sites or participate in virtual workshops and implementation sessions.
  • Implement and optimize agentic AI systems using LLMs and tool orchestration frameworks; conduct prompt engineering, performance evaluation, and fine-tuning.

Requirements

  • 3+ years of experience in ML, NLP, or software engineering, with recent exposure to LLMs and agentic workflows.
  • Strong programming skills in Python and relevant libraries (Transformers, LangChain, FastAPI, etc.).
  • Experience with cloud infrastructure and deploying ML models in real-world settings.
  • Familiarity with vector databases, retrieval systems, and prompt engineering best practices.
  • Ability to work directly with customers and translate business problems into AI solutions.
  • Occasional travel to customer sites for implementation and workshops.
  • Nice-to-have: prior forward-deployed/customer-facing/consulting engineering experience; exposure to compliance and enterprise deployment constraints (PII, data governance); background in MLOps, data pipelines, orchestration tools (Ray, Airflow, etc.).
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