Senior AI Engineer
New
6
66degreesAI, Cloud, Data
Remote, United StatesContractSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of software engineering, applied AI, machine learning, or related technical experience, including at least 1 year of recent experience designing and delivering production-grade Generative AI solutions.
- Required Skills
- AWSPythonCloud ComputingGCPMachine LearningPrompt EngineeringGenerative AI
Requirements
- Bachelor’s degree (Master’s degree preferred).
- 5+ years of software engineering, applied AI, machine learning, or related technical experience.
- At least 1 year of recent experience designing and delivering production-grade Generative AI solutions.
- Hands-on experience with modern AI systems (LLMs, RAG, agents, structured extraction, classification, workflow automation).
- Proficiency in Python.
- Experience building cloud-native applications on AWS or GCP.
- Experience working with relational and/or NoSQL databases.
- Practical experience with prompt engineering, tool calling, retrieval, model selection, context management, and AI application debugging.
- Experience evaluating AI system quality using metrics, test datasets, human review, and error analysis.
- Strong software engineering fundamentals (system design, testing, scalability, and maintainability).
- Hands-on experience building retrieval-augmented AI applications using vector databases, embeddings, and semantic search.
- Preferred: Experience in healthcare, insurance, financial services, or other regulated industries.
Responsibilities
- Design, build, and maintain production-grade Generative AI, Agentic AI, and machine learning applications.
- Develop evaluation and testing approaches to measure model performance, identify regressions, and improve solution quality.
- Apply LLMs, multimodal models, retrieval-augmented generation (RAG), structured extraction, and tool calling to solve real-world healthcare workflows.
- Partner with Engineering, Product, Data, and Operations teams to integrate AI capabilities into client’s systems.
- Improve the reliability, observability, guardrails, and monitoring of production AI applications.
- Stay current with advancements in AI technologies and apply them pragmatically to deliver business value.
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