• The ideal candidate will have a deep technical background and hands-on experience in building and deploying AI/ML systems, as well as promoting a data-driven culture across the organization
• Additionally, the candidate will have interest in learning about and potentially contributing towards cutting edge open source AI/ML technologies
• Bachelor's degree in Computer Science, Engineering, or a related field
• Fluency in a computing language, e.g. Python, Scala, C++, Java, etc
• Knowledge of AWS Bedrock, OpenAI or similar Generative AI platforms
• Experience with developing production AI/ML systems and platforms at scale, including retrieval-augmented generation (RAG) and embedding workflows
• Experience with LLMOps, CI/CD, and IaC
• Familiar with the full AI/ML lifecycle from model development, training, testing, deployment, monitoring, and iterating
• Knowledge in prompt engineering and guardrails
• Excellent verbal and written communication
• Exceptional troubleshooting and problem solving skills, thrive in a fast-paced, innovative environment
• This position requires the ability to access federal environments and/or have access to protected federal data. As a condition of employment for this position, the successful candidate must be able to submit documentation establishing U.S. Person status (e.g. a U.S. Citizen, National, Lawful Permanent Resident, Refugee, or Asylee. 22 CFR 120.15) upon hire

Desirable

• The Machine Learning team is responsible for building ML models, leveraging Generative AI, and developing infrastructure and platform components to scale up AI/ML operations
• This Machine Learning Engineer role will help build and scale Generative AI use cases from integrating Large Language Models (LLM) to developing reusable platform components
• They would not hesitate to wrangle data, if necessary, and are acutely familiar with what makes an effective and efficient ML team
• They will also show passion generally for the field of applied AI/ML
• Design and implement infrastructure and platform components for leveraging LLM in production
• Tune and optimize LLM to improve response quality and optimize performance
• Build workflows and pipelines to process data from myriad sources into a knowledge base for various use cases
• Collaborate with platform engineering teams to ensure that AI/ML systems integrate successfully into production environments while adhering to performance and availability SLOs
• Participate in project planning, design, development, and code reviews
• Communicate verbally and in writing to business customers and leadership teams with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
• Partnership across Engineering, Product Management, Security and Design teams to solve technical and non-technical challenges