Production AI Engineer: Build, Deploy & Own Systems

DISTYL

New York (NY)

On-site

USD 150,000 - 230,000

Full time

9 days ago
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Benefits offered by this job

Equity options
Healthcare
Collaborative environment
Growth opportunities

Job summary

DISTYL is seeking experienced AI Engineers to design, implement, deploy, and iterate on production AI systems in customer environments. You will translate operational needs into concrete system behavior and own the outcomes with a focus on reliability and value.

You will write clean Python services, integrate AI capabilities with data platforms and APIs, measure performance, debug issues, and collaborate with customers, SMEs, and other DISTYL engineers to balance speed and robustness in

Qualifications

  • 5+ years of software engineering experience.
  • Strong engineering fundamentals across versioning, debugging, testing, and production readiness.
  • Experience building AI systems powered by LLMs or AI models.
  • Ability to navigate ambiguity and deliver pragmatic results.

Responsibilities

  • Design, implement, deploy, and iterate on production AI systems.
  • Collaborate with customers, SMEs, and other Distyl engineers.
  • Translate operational needs into concrete system behavior.
  • Develop Python services and integration with data platforms and APIs.
  • Measure system behavior, debug issues, and improve reliability.
  • Create evaluation frameworks and observability patterns.

Skills

Software engineering
Python
AI systems
Ownership mindset
Travel willingness
Troubleshooting

Job description

DISTYL is seeking experienced AI Engineers to design, implement, deploy, and iterate on production AI systems in customer environments. You will translate operational needs into concrete system behavior and own the outcomes with a focus on reliability and value.

You will write clean Python services, integrate AI capabilities with data platforms and APIs, measure performance, debug issues, and collaborate with customers, SMEs, and other DISTYL engineers to balance speed and robustness in

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