Alphasense

Alphasense

Alphasense is a global leader in providing high-quality gas sensors and air quality monitors to industrial OEMs. With over 25 years of experience, the company offers a wide range of innovative gas sensor technologies for various applications, including...

Industrial Conglomerates
51-250
Founded 1996

Description

  • Research, evaluate, and prototype AI solutions using large language models, computer vision, and machine learning techniques.
  • Design and build scalable, cost-effective AI services and data processing pipelines for high-volume document processing.
  • Develop production systems for document classification, entity extraction, image search, and related content intelligence use cases.
  • Address production AI challenges such as LLM latency, output variance, prompt engineering, and fault tolerance.
  • Architect distributed systems with strong asynchronous, streaming, queuing, caching, and observability practices.
  • Deploy, monitor, and iterate on AI/ML models in a high-volume cloud environment.
  • Serve as the primary technical authority on AI for the content portfolio.
  • Mentor engineers and drive mission-critical initiatives to production.

Requirements

  • 10+ years of professional software engineering experience building complex, data-intensive backend systems.
  • 5+ years of experience building and scaling production-grade services with modern backend frameworks such as FastAPI, Django, Sanic, Spring Boot, or similar.
  • 3+ years of hands-on experience across the full AI/ML lifecycle, from experimentation and prototyping through deployment, monitoring, and iteration.
  • Strong expertise in designing large-scale distributed systems.
  • Knowledge of asynchronous patterns, streaming/queuing/caching strategies, and robust observability practices including logging, metrics, and tracing.
  • Exceptional communication and leadership skills with the ability to influence engineering direction across multiple teams without direct authority.
  • Experience with modern DevOps and MLOps practices, including CI/CD pipelines, Infrastructure as Code, and automated testing frameworks (nice to have).
  • Hands-on experience with Docker and Kubernetes (nice to have).

Interested in this position?

Apply directly on the company website

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