Salary
💰 $161,000 - $269,000 per year
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.).