Flexion-posted 10 days ago
$200,000 - $250,000/Yr
Full-time • Senior
Remote • Madison, WI
101-250 employees

We’re looking for a Senior Artificial Intelligence Engineer to help mature Flexion’s AI application development capabilities. Flexion is committed to providing top-tier AI application development services and is looking for an Artificial Intelligence Engineer to help us advance our capabilities. Most of the work we do is on large-scale, enterprise-wide systems (both commercial and government). At Flexion (an agile software company that’s been delivering excellence for over 25 years), our company culture is built on autonomy, trust, and transparency. We empower teams to remain self-sufficient and self-directed by hiring people who can solve complex problems through collaboration—this means lending a hand and flexing your multi-skilled muscles (research, content, business analysis, information architecture, etc.) as needed. Every member within a cross-functional team is a leader who takes responsibility for the entire team’s success, which mirrors the company’s overall low bureaucracy structure. What the job looks like: You’ll be responsible for building AI applications and helping create AI application capability within Flexion. The project work is primarily remote but may require some client on-site work estimated at <10%.

  • Lead AI application architectural design activities, particularly in the domain of Generative AI.
  • Lead AI application implementation and operations activities.
  • Help Flexion develop or adopt learnable AI development and deployment patterns, and avoid common pitfalls.
  • Coach other engineers in AI application development and operations.
  • Potentially fill roles adjacent to AI application development, if needed.
  • At least 10 years building and operationalizing AI/ML powered applications.
  • At least 5 years experience working in an agile environment.
  • A bachelor's degree or commensurate experience.
  • Knowledge of lean and agile methodologies to deliver a quality product with minimal churn.
  • Strong communication and coaching skills.
  • Production-level experience with the entire ML Lifecycle including training, fine tuning, deploying, monitoring, and retraining
  • Production-level experience using and managing ML Lifecycle tooling including experiment trackers, model registry, feature stores, vector databases and deployment services
  • Production-level experience with scaling services including containerization, container orchestration, load testing, autoscaling, and monitoring
  • Production-level experience with scaling AI and ML specific services including the use of quantization, pruning, distributed computing on accelerated infrastructure, model compilation techniques, and inference graphs
  • Production-level experience with AI cloud deployment strategies and technologies
  • Production-level experience with selecting foundation or pre-trained models and finetuning them for specific use cases
  • Production-level experience with optimizing models and inference pipelines
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