Sr. AI Engineer (Applied AI & ML Systems)
New
United StatesFull-TimeSenior
Salary$160,000 - $205,000 a year
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Job Details
- Experience
- 6+ years of relevant experience
- Required Skills
- PythonMachine LearningData engineeringSoftware EngineeringPrompt Engineering
Requirements
- Bachelor's degree in Computer Science or a related field.
- 6+ years of relevant experience.
- 4+ years of experience in machine learning, applied modeling, data engineering, or software engineering for data-intensive systems.
- 2+ years of experience building LLM-based applications.
- At least 1 year of experience building agentic AI systems.
- Experience building and operating production data pipelines, data platforms, or large-scale data-intensive systems.
- Hands-on experience with context engineering, retrieval-augmented generation (RAG), evaluation frameworks, and prompt engineering.
- Experience designing and implementing agentic AI systems including planning, memory, handoffs, tool orchestration, and human-in-the-loop review.
- Track record of operating AI systems in production, including deployment, monitoring, observability, and versioning.
- Advanced Python skills.
- Experience balancing quality, latency, cost, reliability, and maintainability in production AI.
Responsibilities
- Design, build, and deploy AI solutions powered by ML, LLMs, and agentic AI systems that solve real business problems.
- Define evaluation strategies upfront for each use case, including task success metrics, offline and online evaluation plans, error analysis, and production monitoring requirements.
- Build and improve LLM-based systems using prompt engineering, retrieval-augmented generation (RAG), context engineering, and multi-step agentic workflows.
- Partner closely with product, engineering, data, and business stakeholders to prioritize AI use cases and align on success metrics, operational requirements, and delivery timelines.
- Apply strong production practices across AI systems, including experimentation, versioning, observability, alerting and continuous improvement in production.
- Monitor, troubleshoot, and improve production AI systems by balancing quality, latency, cost, reliability, and maintainability.
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