Senior AI Engineer
New
R
RegScaleSoftware, Compliance
Location: RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8 or more years of software engineering experience with at least 4 years focused on building and operating AI or machine learning systems in production environments
- Required Skills
- Machine LearningData engineeringCI/CD
Requirements
- 8+ years of software engineering experience.
- 4+ years focused on building and operating AI/ML systems in production.
- Demonstrated track record of shipping AI features with full production lifecycle ownership.
- Strong data engineering fundamentals (pipeline design, modeling, transformation, quality).
- Hands-on experience with RAG, vector/graph databases, embedding models, and hybrid retrieval.
- Experience designing/building AI agent systems and orchestration frameworks.
- Understanding of model fine-tuning and evaluation for domain-specific applications.
- Strong software engineering fundamentals applied to AI systems with production-grade rigor.
- Strong written and verbal communication skills.
Responsibilities
- Design, build, and operate AI systems in production with full ownership across reliability, performance, cost, observability, and ongoing model behavior.
- Build and maintain data pipelines that ingest, clean, transform, and version the data AI systems depend on, ensuring quality and traceability from source to model.
- Design and implement retrieval augmented generation pipelines, vector and graph search systems, and hybrid retrieval strategies.
- Fine tune, evaluate, and monitor models against real world performance criteria.
- Architect and build AI agent systems and orchestration layers that coordinate multi step reasoning, tool use, and decision making.
- Build and maintain MCP servers that expose RegScale platform capabilities to AI systems.
- Design reusable AI primitives and frameworks that product and integration teams can build on.
- Integrate AI capabilities into CI/CD pipelines with appropriate testing and evaluation gates.
- Partner with Platform Engineering, Core Engineering, and Compliance as Code teams.
- Proactively identify risks in AI system behavior, data quality, and model performance.
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