Software Engineer, AI Agents
Builds production AI agents and LLM pipelines for marketing workflows, integrating with data warehouses. Requires strong backend architecture skills, product thinking, and creativity with LLMs; senior role emphasizing impact over years of experience.
About the job
Responsibilities
- Quickly iterate and develop proofs of concept (POCs) to explore AI integration into data and marketing workflows.
- Make key decisions about AI architecture and frameworks.
- Build production data agents using company data to answer analytics and data science questions about users and marketing efforts.
- Develop deep understanding of user workflows to create seamless AI-assisted user experiences.
Requirements
- Strong customer and product thinking.
- Highly creative with LLM applications.
- Ability to rapidly prototype and architect production LLM pipelines.
- Strong technical skills, particularly in backend architecture.
- Product intuition.
Compensation
Salary Range: $180,000 - $320,000 USD per year (location independent, remote-first policy).
Skills
LLMs, AI Agents, Backend Architecture, Python, Kubernetes, Snowflake, Databricks, Llm Frameworks, Data Pipelines, Agentic AI
Similar jobs
ML Engineering jobsBuild and deploy agentic systems that power AI-driven creative video workflows. The role requires 5+ years of experience, production ML or agentic pipeline development, context engineering, and expertise in evaluation and agent infrastructure.
Build and advance agentic machine-learning systems for multimodal creative tasks, with a focus on video understanding, reasoning, control, and tool use. The role requires strong production ML or agent-pipeline experience and deep knowledge of modern LLM techniques.
Build evaluation methods, RL environments, agent tooling, and scalable infrastructure that make subjective qualities such as design and taste measurable for frontier AI models. The role requires experience with evaluations, RL environments, ML or post-training, plus strong backend engineering skills.
Build and scale generative video and multimodal models, optimizing training and inference for efficiency, throughput, and ultra-low latency. The role requires deep learning systems expertise, strong PyTorch/CUDA experience, and the ability to move research models into production.
Build production-grade AI agents, evaluation infrastructure, and developer tooling that make AI-assisted engineering faster, safer, and reusable across teams. The role requires software engineering experience, platform or internal developer-product experience, and hands-on expertise with LLM integration and orchestration.