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Principal Software Engineer – Generative AI
| Company | Blue Yonder |
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| Location | Dallas, TX, USA |
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| Salary | $167076 – $216924 |
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| Type | Full-Time |
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| Degrees | |
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| Experience Level | Expert or higher |
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Requirements
- Minimum 12+ years of software development experience, with a strong foundation in software engineering skills, design patterns, and building scalable systems.
- Minimum 3+ years of engineering leadership in architecture and design.
- Deep expertise in distributed systems, high-performance computing, and software architecture.
- Demonstrated ability to lead cross-functional technical initiatives with significant business impact.
- Strong proficiency in Python and experience with ML frameworks like PyTorch, TensorFlow, or JAX.
- Strong understanding of ML infrastructure, model serving architectures, and AI system optimization.
- Proven track record of shipping products powered by LLMs, RAG systems, and/or AI agents in production.
- Exceptional problem-solving skills and strategic thinking abilities.
Responsibilities
- Establish the technical vision and architectural foundations for our generative AI platforms and products.
- Lead the design and implementation of complex, large-scale AI systems that push technical boundaries.
- Design and build scalable, high-performance Generative AI solutions.
- Work closely with data scientists to fine-tune, deploy, and monitor LLMs.
- Optimize inference pipelines to reduce latency and improve efficiency.
- Build distributed AI systems that handle high-throughput workloads.
- Mentor engineering leaders and senior engineers, elevating the technical capabilities of the organization.
- Identify and solve systemic engineering challenges across model training, deployment, and monitoring.
- Establish technical standards, best practices, and architectural patterns for AI engineering.
- Drive technical alignment with product management & UX on roadmap and deliverables.
Preferred Qualifications
- Background in designing and implementing model serving infrastructure for high-throughput, low-latency applications.
- Expertise in model optimization techniques such as quantization, distillation, and efficient inference.
- Experience with AI safety, alignment techniques, and responsible AI deployment.
- Knowledge of cloud-scale infrastructure and distributed computing paradigms.
- Strong technical communication skills and ability to translate complex concepts for diverse audiences.
- History of contributions to open-source projects or technical publications in relevant fields.
