Forward Deployed AI Engineer, Sprinklr

• 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
• Ability to work directly with customers and translate business problems into AI solutions
• Familiarity with vector databases, retrieval systems, and prompt engineering best practices

Desirable

• As a Forward Deployed AI Engineer, you will work closely with some of our most strategic enterprise customers to deploy LLM-powered agentic solutions tailored to their specific business challenges.
• You’ll be part of a nimble, high-impact engineering team embedded with customers—building custom agentic workflows, optimizing infrastructure, and shaping product feedback directly into our platform. This is a hands-on engineering role for those who thrive at the edge of product innovation and customer engagement
• 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
• Occasionally travel to customer sites or participate in virtual workshops and implementation sessions
• Work closely with enterprise customers to understand their workflows and define solution architectures involving AI agents, LLMs, and retrieval systems
• Translate real-world customer problems into deployable AI workflows using planning, memory, retrieval, and tool integration
• Implement and optimize agentic AI systems using LLMs and tool orchestration frameworks (e.g., LangChain, RAG pipelines)
• Conduct prompt engineering, performance evaluation, and fine-tuning to improve solution effectiveness and reliability
• Collaborate with product and platform teams to identify reusable patterns and influence roadmap based on deployment learnings
• Serve as a technical point of contact during deployments and early adoption phases
• Capture feedback from real-world usage and help prioritize improvements and fixes