Principal GenAI Systems Engineer

SPECTRAFOR is seeking a Principal Generative AI Systems Engineer responsible for designing, developing, and deploying applications that leverage generative AI models. The role involves collaborating with machine learning and software engineers to create a scalable generative AI application, focusing on both front-end and back-end development, and ensuring optimal performance of AI-driven systems.

Responsibilities:

• Design, configure and optimize the GenAI-tech stack including: LLM, Vector DB, Encoder / Decoder, prompt framework (ex. DSPy) and supporting cloud compute and service resources
• Design and implement RAG pipelines that enhance generative AI models by integrating external data sources
• Architect and engineer efficient retrieval systems that can fetch relevant data from databases, knowledge graphs, or external APIs to augment AI-generated responses
• Develop prompting pipelines that leverage context and retrieved information to generate accurate and contextually relevant responses
• Collaborate with machine learning engineers to implement advanced techniques such as vector search, semantic search, and embeddings to improve data retrieval accuracy
• Build and maintain robust pipelines for data retrieval, preprocessing, and integration into the generation process
• Implement automated testing frameworks to validate the performance of RAG and prompting pipelines
• Ensure that the retrieval and generation pipelines are scalable, reliable, and maintainable
• Continuously monitor and refine pipelines to improve efficiency and reduce latency
• Implement monitoring, logging, and alerting to maintain system health and uptime
• Collaborate with cross-functional teams including UX/UI designers, product managers, and DevOps engineers to deliver high-quality products
• Collaborate with DataML Engineers, Integration Engineers & GenAI Engineers for customer-specific deployments & configurations
• Write clean, maintainable code and conduct code reviews
• Document technical architecture, processes, and best practices

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• A strong foundation in software engineering principles is essential
• Additional coursework or certifications in AI/ML or data science is a plus
• 5+ years of professional experience in complex systems engineering, with a strong focus on AI-driven applications
• Proven experience in integrating and deploying machine learning models, particularly in generative AI (e.g., GPT, GANs, VAE, etc.)
• Demonstrated experience in architecting, engineering and deploying RAG pipelines for generative models and complex prompting systems
• Familiarity with Python-based APIs
• Masters degree in Computer Science, Software Engineering or a related field
• Experience with scalable and high-performance application development in a cloud environment (AWS, GCP, Azure)
• Familiarity with technologies and/or data architectures such as: Product Analytics (e.g Pendo, Mixpanel), and Observability systems (e.g. Grafana, New Relic, Dynatrace)