Sr. AI Engineer (Applied AI & ML Systems)
New
Based in the United StatesFull-TimeSenior
Salary$160,000 – $205,000
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Job Details
- Experience
- Bachelor’s degree in Computer Science or related field with 6+ years of relevant technical experience.
- Required Skills
- PythonMachine LearningData engineeringPrompt EngineeringDistributed Systems
Requirements
- Bachelor’s degree in Computer Science or related field with 6+ years of relevant technical experience.
- 4+ years of experience in Machine Learning, Data Engineering, or Software Engineering for data-intensive systems.
- 2+ years of experience building LLM-based applications, including at least 1 year working with agentic AI systems.
- Strong hands-on experience with RAG systems, prompt engineering, context engineering, and LLM optimization techniques.
- Experience building and operating production-grade data pipelines or large-scale AI/ML systems.
- Advanced Python skills with experience taking AI systems from prototype to production.
- Strong understanding of AI system evaluation, monitoring, observability, and performance tuning.
- Experience balancing system trade-offs including cost, latency, reliability, and scalability.
- Strong collaboration and communication skills across technical and non-technical stakeholders.
- Ability to work effectively in ambiguous environments with a strong ownership and problem-solving mindset.
Responsibilities
- Design, build, and deploy AI systems using machine learning, LLMs, retrieval-augmented generation (RAG), and agentic workflows.
- Define evaluation frameworks and success metrics upfront, including offline/online testing, monitoring strategies, and error analysis.
- Develop and optimize LLM-powered applications using prompt engineering, context engineering, and multi-step reasoning pipelines.
- Build and maintain production-grade AI systems, including data pipelines, model serving infrastructure, and observability tools.
- Collaborate with cross-functional teams to prioritize AI use cases and align on performance, delivery, and business impact goals.
- Monitor and improve deployed AI systems by balancing quality, latency, cost, reliability, and maintainability.
- Implement experimentation frameworks, versioning strategies, and continuous improvement cycles for deployed models.
- Design agentic AI systems with planning, tool orchestration, memory, and human-in-the-loop workflows.
- Support deployment of scalable AI solutions using cloud-native and distributed system architectures.
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