Berkley Hunt · 13 hours ago
Artificial Intelligence Engineer
Berkley Hunt is a fast-growing, Series B technology company focused on redefining manufacturing engineering through innovative AI solutions. They are seeking an AI Engineer to design, build, and deploy impactful AI features, collaborating with cross-functional teams to integrate AI capabilities into their platform.
Responsibilities
Develop and fine-tune large language models (LLMs) for classifying aerospace engineering text, categorizing, and linking requirements across the platform
Implement end-to-end ML features from product requirements to production deployment, including backend infrastructure
Collaborate cross-functionally with app engineers, infrastructure, and security engineers to integrate AI capabilities seamlessly into our platform
Design and maintain reproducible training pipelines ensuring model consistency across different environments
Optimize model training processes, inference performance, and associated cloud infrastructure costs
Establish MLOps best practices for versioning, monitoring, and maintaining AI systems in production
Mentor team members on AI concepts and best practices to build organizational knowledge
Qualification
Required
5+ years of professional experience developing AI/ML solutions in production environments
Strong expertise in NLP, particularly with transformer-based models (BERT, GPT, etc.)
Experience taking ML features from concept to production without extensive specialist support
Full-stack development capabilities to build complete AI features
Cross-functional collaboration skills and the ability to communicate complex AI concepts to non-specialists
Independent problem-solving abilities and resourcefulness when tackling novel AI challenges
Product thinking – ability to translate business requirements into pragmatic AI solutions
Experience with MLOps tools and practices (model versioning, experiment tracking, CI/CD for ML)
Demonstrated ability to manage and optimize AWS ML infrastructure costs and performance
Experience with containerization for ML workloads ensuring reproducibility across development and production environments
Background in implementing retrieval systems, semantic search, or vector databases
Adaptability and pragmatism – knowing when to use simple solutions versus building complex systems