Mid-Level Generative Artificial Intelligence (GenAI) Search Solutions Developer

Boeing•Mesa, AZ

6d•Hybrid

About The Position

The GenAI Family Product is seeking a highly motivated Mid-Level GenAI Search Solutions Developer to join our team in Mesa, AZ; Hazelwood, MO; or Seattle, WA. You will be responsible for three core search capabilities: Enterprise Search, Semantic Search, and GenAI Search. This role sits at the intersection of engineering and data science, equally focused on building and shipping production features and on model experimentation and tuning. The ideal candidate is comfortable moving between research-oriented work and scalable system implementation.

Requirements

Bachelor’s degree or higher
3+ years of experience in programming languages such as JavaScript, C#, Python, SQL, etc.
1+ years of experience with Angular
1+ years of experience in web-based development, leveraging ReactJS, Redux, JavaScript, TypeScript, HTML, CSS
1+ years of experience with RESTFUL APIs
1+ years of experience with GitLab, Azure DevOps and CI/CD (Continuous Integration and Continuous Delivery (CI/CD)
Experience with Natural Language Processing (NLP) fundamentals including but not limited to word embeddings, attention mechanisms, sequence-to-sequence models, and transformers
Experience researching and applying large language and generative AI models

Nice To Haves

Master’s degree in Computer Science, Artificial Intelligence, Information Systems or related fields
1+ years of experience with A/B testing and relevance evaluation frameworks
1+ years of experience with cloud infrastructure (Azure, AWS, GCP)
Experience with vector databases
Experience with GenAI product development in an enterprise context
Experience with enterprise search platforms (e.g., Sinequa, Goldfire, Elastic, Microsoft Search)
Experience with machine learning, NLP, or Knowledge Graphs

Responsibilities

Design, develop, and maintain features across Enterprise Search, Semantic Search, and GenAI Search platforms tailored to business partners requirements
Fine-tune and evaluate large language models and embedding models for search relevance and retrieval quality
Build and optimize retrieval pipelines including vector search, hybrid retrieval, re-ranking systems, Application Programming Interfaces (APIs) and backend integrations
Collaborate with product and stakeholders to translate requirements into technical solutions
Instrument experiments, measure model performance, and iterate based on user needs and metrics
Participate in code and design reviews
Maintain high standards for production-quality code