AI Application Engineer
Skills
- Production experience deploying LLMs and conversational AI systems.
- Practical knowledge of RLHF, DPO, RAG, chain-of-thought reasoning, and agentic communication protocols (e.g., MCP).
- Proficiency in Python and experience with Java or C++ for integration tasks.
- Experience with cloud platforms (AWS, GCP, Azure) and containerized deployments (Docker, Kubernetes).
- Familiarity with fine-tuning libraries, evaluation frameworks, and inference/serving engines.
Responsibilities
- Fine-tune, align, and optimize LLMs and conversational AI models (training, RLHF, DPO workflows).
- Integrate conversational agents with tools and agentic protocols (MCP and similar) to extend capabilities.
- Design, implement, test, deploy, and maintain production software following company quality and security practices.
- Build and maintain inference and serving pipelines, including deployment strategies on cloud platforms.
- Collaborate with cross-functional teams to define requirements and deliver scalable solutions.
- Run model evaluation, testing, and performance optimization; monitor production behavior and reliability.
- Participate in agile planning and stay current with advances in generative AI and agent engineering.
Education
- Bachelor's degree in Computer Science, Machine Learning, Engineering, or related field (or equivalent experience).
- Master's degree in a relevant field preferred.
Benefits
- Medical (HSA available), dental, and vision insurance.
- Short-term & long-term disability (company-paid) and life & AD&D (company-paid).
- 401(k) with company match.
- 10 paid holidays plus company holiday week closure and flexible time off policy.
- Work from home / remote-friendly role.
Average salary estimate
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We are going to build the very best engine to deliver the very best next action to change behavior.
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